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Text summarization research has undergone several significant transformations with the advent of deep neural networks, pre-trained language models (PLMs), and recent large language models (LLMs).
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 1901
Earlier work this paper cites.
Sherlock: A System for Interactive Summarization of Large Text Collections
PVS Avinesh, Carsten Binnig, Benjamin Hättasch, Christian M Meyer, and Orkan Özyurt. 2018 · 1905
Earlier work this paper cites.
Large language models in medicine
Arun James Thirunavukarasu, Darren Shu Jeng Ting, Kabilan Elangovan, Laura Gutierrez, Ting Fang Tan, and Daniel Shu Wei Ting. 2023 · 1940
Earlier work this paper cites.
The automatic creation of literature abstracts
Hans Peter Luhn. 1958 · 1958
Earlier work this paper cites.
New methods in automatic extracting
Harold P Edmundson. 1969 · 1969
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
The anatomy of a large-scale hypertextual web search engine
Sergey Brin and Lawrence Page. 1998 · 1998
Earlier work this paper cites.
The use of MMR, diversity-based reranking for reordering documents and producing summaries. In Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval . 335–336
Jaime Carbonell and Jade Goldstein. 1998 · 1998
Earlier work this paper cites.
Advances in automatic text summarization
Inderjeet Mani and Mark T Maybury. 1999 · 1999
Earlier work this paper cites.
Interpretable Multi-Headed Attention for Abstractive Summarization at Controllable Lengths
Ritesh Sarkhel, Moniba Keymanesh, Arnab Nandi, and Srinivasan Parthasarathy. 2020 · 2002
Earlier work this paper cites.
Latent dirichlet allocation
David M Blei, Andrew Y Ng, and Michael I Jordan. 2003 · 2003
Earlier work this paper cites.
Automatic evaluation of summaries using n-gram co-occurrence statistics. In Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics . 150–157
Chin-Yew Lin and Eduard Hovy. 2003 · 2003
Earlier work this paper cites.
Lexrank: Graph-based lexical centrality as salience in text summarization
Günes Erkan and Dragomir R Radev. 2004 · 2004
Earlier work this paper cites.
Textrank: Bringing order into text. In Proceedings of the 2004 conference on empirical methods in natural language processing . 404–411
Rada Mihalcea and Paul Tarau. 2004 · 2004
Earlier work this paper cites.
Evaluating content selection in summarization: The pyramid method. In Proceedings of the human language technology conference of the north american chapter of the association for computational linguistics: Hlt-naacl 2004 . 145–152
Ani Nenkova and Rebecca J Passonneau. 2004 · 2004
Earlier work this paper cites.
A study of global inference algorithms in multi-document summarization. In European Conference on Information Retrieval . Springer, 557–564
Ryan McDonald. 2007 · 2007
Earlier work this paper cites.
DUC in context
Paul Over, Hoa Dang, and Donna Harman. 2007 · 2007
Earlier work this paper cites.
Intrinsic vs. extrinsic evaluation measures for referring expression generation. In Proceedings of ACL-08: HLT, Short Papers . 197–200
Anja Belz and Albert Gatt. 2008 · 2008
Earlier work this paper cites.
Overview of the TAC 2008 update summarization task.. In TAC
Hoa Trang Dang, Karolina Owczarzak, et al · 2008
Earlier work this paper cites.
The new york times annotated corpus
Evan Sandhaus. 2008 · 2008
Earlier work this paper cites.
Text summarization model based on maximum coverage problem and its variant. In Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009) . 781–789
Hiroya Takamura and Manabu Okumura. 2009 · 2009
Earlier work this paper cites.
A class of submodular functions for document summarization. In Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies . 510–520
Hui Lin and Jeff Bilmes. 2011 · 2011
Earlier work this paper cites.
Determinantal point processes for machine learning
Alex Kulesza, Ben Taskar, et al · 2012
Earlier work this paper cites.
Single-document summarization as a tree knapsack problem. In Proceedings of the 2013 conference on empirical methods in natural language processing . 1515–1520
Tsutomu Hirao, Yasuhisa Yoshida, Masaaki Nishino, Norihito Yasuda, and Masaaki Nagata. 2013 · 2013
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2013
Earlier work this paper cites.
Convolutional Neural Networks for Sentence Classification
Yoon Kim. 2014 · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) . 1532–1543
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014 · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
Earlier work this paper cites.
Quasi real-time summarization for consumer videos. In Proceedings of the IEEE conference on computer vision and pattern recognition . 2513–2520
Bin Zhao and Eric P Xing. 2014 · 2014
Earlier work this paper cites.
Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Earlier work this paper cites.
A Neural Attention Model for Abstractive Sentence Summarization. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing . 379–389
Alexander M Rush, Sumit Chopra, and Jason Weston. 2015 · 2015
Earlier work this paper cites.
Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. 2015 · 2015
Earlier work this paper cites.
Optimizing sentence modeling and selection for document summarization. In Twenty-fourth international joint conference on artificial intelligence
Wenpeng Yin and Yulong Pei. 2015 · 2015
Earlier work this paper cites.
Topic concentration in query focused summarization datasets. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 30
Tal Baumel, Raphael Cohen, and Michael Elhadad. 2016 · 2016
Earlier work this paper cites.
AttSum: Joint Learning of Focusing and Summarization with Neural Attention. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers . 547–556
Ziqiang Cao, Wenjie Li, Sujian Li, Furu Wei, and Yanran Li. 2016 · 2016
Earlier work this paper cites.
Neural Summarization by Extracting Sentences and Words. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 484–494
Jianpeng Cheng and Mirella Lapata. 2016 · 2016
Earlier work this paper cites.
Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations
Thomas N Kipf and Max Welling. 2016 · 2016
Earlier work this paper cites.
Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond. In Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning . Association for Computational Linguistics, Berlin, Germany, 280–290
Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Çağlar Gulçehre, and Bing Xiang. 2016 · 2016
Earlier work this paper cites.
Summarunner: A recurrent neural network based sequence model for extractive summarization of documents. In Thirty-first AAAI conference on artificial intelligence
Ramesh Nallapati, Feifei Zhai, and Bowen Zhou. 2017 · 2017
Earlier work this paper cites.
Learning to score system summaries for better content selection evaluation.. In Proceedings of the Workshop on New Frontiers in Summarization . 74–84
Maxime Peyrard, Teresa Botschen, and Iryna Gurevych. 2017 · 2017
Earlier work this paper cites.
Get To The Point: Summarization with Pointer-Generator Networks. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 1073–1083
Abigail See, Peter J Liu, and Christopher D Manning. 2017 · 2017
Earlier work this paper cites.
Interactive abstractive summarization for event news tweets. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations . 109–114
Ori Shapira, Hadar Ronen, Meni Adler, Yael Amsterdamer, Judit Bar-Ilan, and Ido Dagan. 2017 · 2017
Earlier work this paper cites.
Attention is all you need. In Advances in neural information processing systems . 5998–6008
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Graph-based Neural Multi-Document Summarization. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017) . 452–462
Michihiro Yasunaga, Rui Zhang, Kshitijh Meelu, Ayush Pareek, Krishnan Srinivasan, and Dragomir Radev. 2017 · 2017
Earlier work this paper cites.
Deep communicating agents for abstractive summarization
Asli Celikyilmaz, Antoine Bosselut, Xiaodong He, and Yejin Choi. 2018 · 2018
Earlier work this paper cites.
Unsupervised neural multi-document abstractive summarization
Eric Chu and Peter J Liu. 2018 · 2018
Earlier work this paper cites.
A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) . 615–621
Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, and Nazli Goharian. 2018 · 2018
Earlier work this paper cites.
A survey on neural network-based summarization methods
Yue Dong. 2018 · 2018
Earlier work this paper cites.
Banditsum: Extractive summarization as a contextual bandit
Yue Dong, Yikang Shen, Eric Crawford, Herke van Hoof, and Jackie Chi Kit Cheung. 2018 · 2018
Earlier work this paper cites.
Bottom-Up Abstractive Summarization. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . 4098–4109
Sebastian Gehrmann, Yuntian Deng, and Alexander M Rush. 2018 · 2018
Earlier work this paper cites.
Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) . 708–719
Max Grusky, Mor Naaman, and Yoav Artzi. 2018 · 2018
Earlier work this paper cites.
Wikihow: A large scale text summarization dataset
Mahnaz Koupaee and William Yang Wang. 2018 · 2018
Earlier work this paper cites.
Adapting the neural encoder-decoder framework from single to multi-document summarization
Logan Lebanoff, Kaiqiang Song, and Fei Liu. 2018 · 2018
Earlier work this paper cites.
Toward abstractive summarization using semantic representations
Fei Liu, Jeffrey Flanigan, Sam Thomson, Norman Sadeh, and Noah A Smith. 2018a · 2018
Earlier work this paper cites.
Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization. In 2018 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, 1797–1807
Shashi Narayan, Shay Cohen, and Maria Lapata. 2018a · 2018
Earlier work this paper cites.
Ranking Sentences for Extractive Summarization with Reinforcement Learning. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) . 1747–1759
Shashi Narayan, Shay B Cohen, and Mirella Lapata. 2018b · 2018
Earlier work this paper cites.
A Deep Reinforced Model for Abstractive Summarization. In International Conference on Learning Representations
Romain Paulus, Caiming Xiong, and Richard Socher. 2018 · 2018
Earlier work this paper cites.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
Earlier work this paper cites.
Overview of the TREC 2018 Real-Time Summarization Track.. In TREC
Royal Sequiera, Luchen Tan, and Jimmy Lin. 2018 · 2018
Earlier work this paper cites.
Structure-infused copy mechanisms for abstractive summarization
Kaiqiang Song, Lin Zhao, and Fei Liu. 2018 · 2018
Earlier work this paper cites.
Graph Attention Networks. In International Conference on Learning Representations
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018
Earlier work this paper cites.
Improving automatic source code summarization via deep reinforcement learning. In Proceedings of the 33rd ACM/IEEE international conference on automated software engineering . 397–407
Yao Wan, Zhou Zhao, Min Yang, Guandong Xu, Haochao Ying, Jian Wu, and Philip S Yu. 2018 · 2018
Earlier work this paper cites.
Aspect and sentiment aware abstractive review summarization. In Proceedings of the 27th international conference on computational linguistics . 1110–1120
Min Yang, Qiang Qu, Ying Shen, Qiao Liu, Wei Zhao, and Jia Zhu. 2018 · 2018
Earlier work this paper cites.
Neural latent extractive document summarization
Xingxing Zhang, Mirella Lapata, Furu Wei, and Ming Zhou. 2018 · 2018
Earlier work this paper cites.
Neural Document Summarization by Jointly Learning to Score and Select Sentences. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 654–663
Qingyu Zhou, Nan Yang, Furu Wei, Shaohan Huang, Ming Zhou, and Tiejun Zhao. 2018 · 2018
Earlier work this paper cites.
MSMO: Multimodal summarization with multimodal output. In Proceedings of the 2018 Conference on empirical methods in natural language processing . 4154–4164
Junnan Zhu, Haoran Li, Tianshang Liu, Yu Zhou, Jiajun Zhang, and Chengqing Zong. 2018 · 2018
Earlier work this paper cites.
Summary Level Training of Sentence Rewriting for Abstractive Summarization. In Proceedings of the 2nd Workshop on New Frontiers in Summarization . 10–20
Sanghwan Bae, Taeuk Kim, Jihoon Kim, and Sang-goo Lee. 2019 · 2019
Earlier work this paper cites.
Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document Summarization. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics . 1027–1038
Sangwoo Cho, Logan Lebanoff, Hassan Foroosh, and Fei Liu. 2019 · 2019
Earlier work this paper cites.
Sentence mover’s similarity: Automatic evaluation for multi-sentence texts. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics . 2748–2760
Elizabeth Clark, Asli Celikyilmaz, and Noah A Smith. 2019 · 2019
Earlier work this paper cites.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) . 4171–4186
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
Unified language model pre-training for natural language understanding and generation
Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2019 · 2019
Earlier work this paper cites.
Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics . 1074–1084
Alexander Richard Fabbri, Irene Li, Tianwei She, Suyi Li, and Dragomir Radev. 2019 · 2019
Earlier work this paper cites.
SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization. In Proceedings of the 2nd Workshop on New Frontiers in Summarization . 70–79
Bogdan Gliwa, Iwona Mochol, Maciej Biesek, and Aleksander Wawer. 2019 · 2019
Earlier work this paper cites.
Abstractive Summarization of Reddit Posts with Multi-level Memory Networks. In Proceedings of NAACL-HLT . 2519–2531
Byeongchang Kim, Hyunwoo Kim, and Gunhee Kim. 2019 · 2019
Earlier work this paper cites.
BillSum: A Corpus for Automatic Summarization of US Legislation
Anastassia Kornilova and Vlad Eidelman. 2019 · 2019
Earlier work this paper cites.
Albert: A lite bert for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2019 · 2019
Earlier work this paper cites.
Scoring sentence singletons and pairs for abstractive summarization. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics . 2175–2189
Logan Lebanoff, Kaiqiang Song, Franck Dernoncourt, Doo Soon Kim, Seokhwan Kim, Walter Chang, and Fei Liu. 2019 · 2019
Earlier work this paper cites.
Text Summarization with Pretrained Encoders. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) . 3730–3740
Yang Liu and Mirella Lapata. 2019b · 2019
Earlier work this paper cites.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 2019
Earlier work this paper cites.
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) . 3982–3992
Nils Reimers and Iryna Gurevych. 2019 · 2019
Earlier work this paper cites.
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019 · 2019
Earlier work this paper cites.
Answers unite! unsupervised metrics for reinforced summarization models
Thomas Scialom, Sylvain Lamprier, Benjamin Piwowarski, and Jacopo Staiano. 2019 · 2019
Earlier work this paper cites.
BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics . 2204–2213
Eva Sharma, Chen Li, and Lu Wang. 2019 · 2019
Earlier work this paper cites.
MASS: Masked Sequence to Sequence Pre-training for Language Generation. In International Conference on Machine Learning . PMLR, 5926–5936
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. 2019 · 2019
Earlier work this paper cites.
Extractive summarization of long documents by combining global and local context
Wen Xiao and Giuseppe Carenini. 2019 · 2019
Earlier work this paper cites.
Graph convolutional networks for text classification. In Proceedings of the AAAI conference on artificial intelligence , Vol. 33. 7370–7377
Liang Yao, Chengsheng Mao, and Yuan Luo. 2019 · 2019
Earlier work this paper cites.
This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics . 446–456
Rui Zhang and Joel Tetreault. 2019 · 2019
Earlier work this paper cites.
MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance
Wei Zhao, Maxime Peyrard, Fei Liu, Yang Gao, Christian M Meyer, and Steffen Eger. 2019 · 2019
Earlier work this paper cites.
Sentence Centrality Revisited for Unsupervised Summarization. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics . 6236–6247
Hao Zheng and Mirella Lapata. 2019 · 2019
Earlier work this paper cites.
A closer look at data bias in neural extractive summarization models
Ming Zhong, Danqing Wang, Pengfei Liu, Xipeng Qiu, and Xuanjing Huang. 2019 · 2019
Earlier work this paper cites.
Better fine-tuning by reducing representational collapse
Armen Aghajanyan, Akshat Shrivastava, Anchit Gupta, Naman Goyal, Luke Zettlemoyer, and Sonal Gupta. 2020 · 2020
Earlier work this paper cites.
Longformer: The long-document transformer
Iz Beltagy, Matthew E Peters, and Arman Cohan. 2020 · 2020
Earlier work this paper cites.
Factual error correction for abstractive summarization models
Meng Cao, Yue Dong, Jiapeng Wu, and Jackie Chi Kit Cheung. 2020 · 2020
Cited alongside, same era.
Discourse-aware unsupervised summarization of long scientific documents
Yue Dong, Andrei Mircea, and Jackie CK Cheung. 2020 · 2020
Cited alongside, same era.
Esin Durmus, He He, and Mona Diab. 2020 · 2020
Cited alongside, same era.
SUPERT: Towards new frontiers in unsupervised evaluation metrics for multi-document summarization
Yang Gao, Wei Zhao, and Steffen Eger. 2020 · 2020
Cited alongside, same era.
InstructPTS: Instruction-Tuning LLMs for Product Title Summarization. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track . 663–674
Besnik Fetahu, Zhiyu Chen, Oleg Rokhlenko, and Shervin Malmasi. 2023 · 2023
Later among the works it cites.
GPTScore: Evaluate as You Desire
Jinlan Fu, See-Kiong Ng, Zhengbao Jiang, and Pengfei Liu. 2023 · 2023
Later among the works it cites.
Bias and fairness in large language models: A survey
Isabel O Gallegos, Ryan A Rossi, Joe Barrow, Md Mehrab Tanjim, Sungchul Kim, Franck Dernoncourt, Tong Yu, Ruiyi Zhang, and Nesreen K Ahmed. 2023 · 2023
Later among the works it cites.
Human-like summarization evaluation with chatgpt
Mingqi Gao, Jie Ruan, Renliang Sun, Xunjian Yin, Shiping Yang, and Xiaojun Wan. 2023 · 2023
Later among the works it cites.
MiniLLM: Knowledge distillation of large language models. In The Twelfth International Conference on Learning Representations
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A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . 1302–1308
Demian Gholipour Ghalandari, Chris Hokamp, John Glover, Georgiana Ifrim, et al · 2020
Cited alongside, same era.
Evaluating Factuality in Generation with Dependency-level Entailment. In Findings of the Association for Computational Linguistics: EMNLP 2020
Tanya Goyal and Greg Durrett. 2020 · 2020
Cited alongside, same era.
Knowledge graph-augmented abstractive summarization with semantic-driven cloze reward
Luyang Huang, Lingfei Wu, and Lu Wang. 2020 · 2020
Cited alongside, same era.
Dr. summarize: Global summarization of medical dialogue by exploiting local structures
Anirudh Joshi, Namit Katariya, Xavier Amatriain, and Anitha Kannan. 2020 · 2020
Cited alongside, same era.
Evaluating the Factual Consistency of Abstractive Text Summarization. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) . Association for Computational Linguistics, Online, 9332–9346
Wojciech Kryscinski, Bryan McCann, Caiming Xiong, and Richard Socher. 2020 · 2020
Cited alongside, same era.
Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al · 2020
Cited alongside, same era.
Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) . 8068–8074
Yao Lu, Yue Dong, and Laurent Charlin. 2020 · 2020
Cited alongside, same era.
On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020 · 2020
Cited alongside, same era.
Yuxian Gu, Li Dong, Furu Wei, and Minlie Huang. 2023 · 2023
Later among the works it cites.
Kung-Hsiang Huang, Philippe Laban, Alexander R Fabbri, Prafulla Kumar Choubey, Shafiq Joty, Caiming Xiong, and Chien-Sheng Wu. 2023 · 2023
Later among the works it cites.
Multi-Dimensional Evaluation of Text Summarization with In-Context Learning
Sameer Jain, Vaishakh Keshava, Swarnashree Mysore Sathyendra, Patrick Fernandes, Pengfei Liu, Graham Neubig, and Chunting Zhou. 2023 · 2023
Later among the works it cites.
Survey of hallucination in natural language generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023 · 2023
Later among the works it cites.
Zero-shot Faithfulness Evaluation for Text Summarization with Foundation Language Model. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing . 11017–11031
Qi Jia, Siyu Ren, Yizhu Liu, and Kenny Zhu. 2023 · 2023
Later among the works it cites.
Exploiting Pseudo Image Captions for Multimodal Summarization
Chaoya Jiang, Rui Xie, Wei Ye, Jinan Sun, and Shikun Zhang. 2023 · 2023
Later among the works it cites.
Jaehun Jung, Peter West, Liwei Jiang, Faeze Brahman, Ximing Lu, Jillian Fisher, Taylor Sorensen, and Yejin Choi. 2023 · 2023
Later among the works it cites.
Speech summarization of long spoken document: Improving memory efficiency of speech/text encoders. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 1–5
Takatomo Kano, Atsunori Ogawa, Marc Delcroix, Roshan Sharma, Kohei Matsuura, and Shinji Watanabe. 2023 · 2023
Later among the works it cites.
ChatGPT for good? On opportunities and challenges of large language models for education
Enkelejda Kasneci, Kathrin Seßler, Stefan Küchemann, Maria Bannert, Daryna Dementieva, Frank Fischer, Urs Gasser, Georg Groh, Stephan Günnemann, Eyke Hüllermeier, et al · 2023
Later among the works it cites.
Natural language processing in the legal domain
Daniel Martin Katz, Dirk Hartung, Lauritz Gerlach, Abhik Jana, and Michael J Bommarito II. 2023 · 2023
Later among the works it cites.
Mlask: multimodal summarization of video-based news articles. In Findings of the Association for Computational Linguistics: EACL 2023 . 910–924
Mateusz Krubiński and Pavel Pecina. 2023 · 2023
Later among the works it cites.
SummEdits: Measuring LLM Ability at Factual Reasoning Through The Lens of Summarization. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing . 9662–9676
Philippe Laban, Wojciech Kryściński, Divyansh Agarwal, Alexander Richard Fabbri, Caiming Xiong, Shafiq Joty, and Chien-Sheng Wu. 2023 · 2023
Later among the works it cites.
Building Real-World Meeting Summarization Systems using Large Language Models: A Practical Perspective. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track . 343–352
Md Tahmid Rahman Laskar, Xue-Yong Fu, Cheng Chen, and Shashi Bhushan Tn. 2023a · 2023
Later among the works it cites.
Can Large Language Models Fix Data Annotation Errors? An Empirical Study Using Debatepedia for Query-Focused Text Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2023 . 10245–10255
Md Tahmid Rahman Laskar, Mizanur Rahman, Israt Jahan, Enamul Hoque, and Jimmy Huang. 2023b · 2023
Later among the works it cites.
Loftq: Lora-fine-tuning-aware quantization for large language models
Yixiao Li, Yifan Yu, Chen Liang, Pengcheng He, Nikos Karampatziakis, Weizhu Chen, and Tuo Zhao. 2023 · 2023
Later among the works it cites.
Videoxum: Cross-modal visual and textural summarization of videos
Jingyang Lin, Hang Hua, Ming Chen, Yikang Li, Jenhao Hsiao, Chiuman Ho, and Jiebo Luo. 2023 · 2023
Later among the works it cites.
Neural Abstractive Summarization for Long Text and Multiple Tables
Shuaiqi Liu, Jiannong Cao, Zhongfen Deng, Wenting Zhao, Ruosong Yang, Zhiyuan Wen, and S Yu Philip. 2023a · 2023
Later among the works it cites.
Yixin Liu, Alexander R Fabbri, Jiawen Chen, Yilun Zhao, Simeng Han, Shafiq Joty, Pengfei Liu, Dragomir Radev, Chien-Sheng Wu, and Arman Cohan. 2023b · 2023
Later among the works it cites.
On Learning to Summarize with Large Language Models as References
Yixin Liu, Alexander R Fabbri, Pengfei Liu, Dragomir Radev, and Arman Cohan. 2023c · 2023
Later among the works it cites.
Gpteval: Nlg evaluation using gpt-4 with better human alignment
Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, and Chenguang Zhu. 2023d · 2023
Later among the works it cites.
ChatGPT as a Factual Inconsistency Evaluator for Abstractive Text Summarization
Zheheng Luo, Qianqian Xie, and Sophia Ananiadou. 2023 · 2023
Later among the works it cites.
ImpressionGPT: an iterative optimizing framework for radiology report summarization with chatGPT
Chong Ma, Zihao Wu, Jiaqi Wang, Shaochen Xu, Yaonai Wei, Zhengliang Liu, Lei Guo, Xiaoyan Cai, Shu Zhang, Tuo Zhang, et al · 2023
Later among the works it cites.
Llm-pruner: On the structural pruning of large language models
Xinyin Ma, Gongfan Fang, and Xinchao Wang. 2023a · 2023
Later among the works it cites.
LLM aided semi-supervision for efficient Extractive Dialog Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2023 . 10002–10009
Nishant Mishra, Gaurav Sahu, Iacer Calixto, Ameen Abu-Hanna, and Issam Laradji. 2023 · 2023
Later among the works it cites.
Using LLM for Improving Key Event Discovery: Temporal-Guided News Stream Clustering with Event Summaries. In Findings of the Association for Computational Linguistics: EMNLP 2023 . 4162–4173
Nishanth Nakshatri, Siyi Liu, Sihao Chen, Dan Roth, Dan Goldwasser, and Daniel Hopkins. 2023 · 2023
Later among the works it cites.
Biases in large language models: origins, inventory, and discussion
Roberto Navigli, Simone Conia, and Björn Ross. 2023 · 2023
Later among the works it cites.
AUTOGEN: A personalized large language model for academic enhancement—Ethics and proof of principle
Sebastian Porsdam Mann, Brian D Earp, Nikolaj Møller, Suren Vynn, and Julian Savulescu. 2023 · 2023
Later among the works it cites.
Dongqi Pu and Vera Demberg. 2023 · 2023
Later among the works it cites.
Summarization is (almost) dead
Xiao Pu, Mingqi Gao, and Xiaojun Wan. 2023 · 2023
Later among the works it cites.
Promptsum: Parameter-efficient controllable abstractive summarization
Mathieu Ravaut, Hailin Chen, Ruochen Zhao, Chengwei Qin, Shafiq Joty, and Nancy Chen. 2023a · 2023
Later among the works it cites.
On Position Bias in Summarization with Large Language Models
Mathieu Ravaut, Shafiq Joty, Aixin Sun, and Nancy F Chen. 2023b · 2023
Later among the works it cites.
Summarizing, simplifying, and synthesizing medical evidence using gpt-3 (with varying success)
Chantal Shaib, Millicent L Li, Sebastian Joseph, Iain J Marshall, Junyi Jessy Li, and Byron C Wallace. 2023 · 2023
Later among the works it cites.
Large language models are not yet human-level evaluators for abstractive summarization. In Findings of the Association for Computational Linguistics: EMNLP 2023 . 4215–4233
Chenhui Shen, Liying Cheng, Xuan-Phi Nguyen, Yang You, and Lidong Bing. 2023 · 2023
Later among the works it cites.
Reflexion: an autonomous agent with dynamic memory and self-reflection
Noah Shinn, Beck Labash, and Ashwin Gopinath. 2023 · 2023
Later among the works it cites.
A simple and effective pruning approach for large language models
Mingjie Sun, Zhuang Liu, Anna Bair, and J Zico Kolter. 2023b · 2023
Later among the works it cites.
Automatic Code Summarization via ChatGPT: How Far Are We?
Weisong Sun, Chunrong Fang, Yudu You, Yun Miao, Yi Liu, Yuekang Li, Gelei Deng, Shenghan Huang, Yuchen Chen, Quanjun Zhang, et al · 2023
Later among the works it cites.
Evaluating the factual consistency of large language models through news summarization. In Findings of the Association for Computational Linguistics: ACL 2023 . 5220–5255
Derek Tam, Anisha Mascarenhas, Shiyue Zhang, Sarah Kwan, Mohit Bansal, and Colin Raffel. 2023 · 2023
Later among the works it cites.
Evaluating large language models on medical evidence summarization
Liyan Tang, Zhaoyi Sun, Betina Idnay, Jordan G Nestor, Ali Soroush, Pierre A Elias, Ziyang Xu, Ying Ding, Greg Durrett, Justin F Rousseau, et al · 2023
Later among the works it cites.
Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al · 2023
Later among the works it cites.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
Later among the works it cites.
Clinical text summarization: Adapting large language models can outperform human experts
Dave Van Veen, Cara Van Uden, Louis Blankemeier, Jean-Benoit Delbrouck, Asad Aali, Christian Bluethgen, Anuj Pareek, Malgorzata Polacin, Eduardo Pontes Reis, Anna Seehofnerova, et al · 2023
Later among the works it cites.
Cross-lingual summarization via chatgpt
Jiaan Wang, Yunlong Liang, Fandong Meng, Zhixu Li, Jianfeng Qu, and Jie Zhou. 2023a · 2023
Later among the works it cites.
Prompt engineering for healthcare: Methodologies and applications
Jiaqi Wang, Enze Shi, Sigang Yu, Zihao Wu, Chong Ma, Haixing Dai, Qiushi Yang, Yanqing Kang, Jinru Wu, Huawen Hu, et al · 2023
Later among the works it cites.
Element-aware Summarization with Large Language Models: Expert-aligned Evaluation and Chain-of-Thought Method
Yiming Wang, Zhuosheng Zhang, and Rui Wang. 2023c · 2023
Later among the works it cites.
Parameter-Efficient Multilingual Summarisation: An Empirical Study
Chenxi Whitehouse, Fantine Huot, Jasmijn Bastings, Mostafa Dehghani, Chu-Cheng Lin, and Mirella Lapata. 2023 · 2023
Later among the works it cites.
Tidybot: Personalized robot assistance with large language models
Jimmy Wu, Rika Antonova, Adam Kan, Marion Lepert, Andy Zeng, Shuran Song, Jeannette Bohg, Szymon Rusinkiewicz, and Thomas Funkhouser. 2023a · 2023
Later among the works it cites.
Large Language Models are Diverse Role-Players for Summarization Evaluation
Ning Wu, Ming Gong, Linjun Shou, Shining Liang, and Daxin Jiang. 2023b · 2023
Later among the works it cites.
Less is More for Long Document Summary Evaluation by LLMs
Yunshu Wu, Hayate Iso, Pouya Pezeshkpour, Nikita Bhutani, and Estevam Hruschka. 2023c · 2023
Later among the works it cites.
Cfsum: A coarse-to-fine contribution network for multimodal summarization
Min Xiao, Junnan Zhu, Haitao Lin, Yu Zhou, and Chengqing Zong. 2023b · 2023
Later among the works it cites.
InheritSumm: A General, Versatile and Compact Summarizer by Distilling from GPT. In Findings of the Association for Computational Linguistics: EMNLP 2023 , Houda Bouamor, Juan Pino, and Kalika Bali (Eds.). Association for Computational Linguistics, Singapore, 13879–13892
Yichong Xu, Ruochen Xu, Dan Iter, Yang Liu, Shuohang Wang, Chenguang Zhu, and Michael Zeng. 2023b · 2023
Later among the works it cites.
Exploring the Limits of ChatGPT for Query or Aspect-based Text Summarization
Xianjun Yang, Yan Li, Xinlu Zhang, Haifeng Chen, and Wei Cheng. 2023 · 2023
Later among the works it cites.
Improving Summarization with Human Edits. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing . 2604–2620
Zonghai Yao, Benjamin Schloss, and Sai Selvaraj. 2023 · 2023
Later among the works it cites.
Improving language models via plug-and-play retrieval feedback
Wenhao Yu, Zhihan Zhang, Zhenwen Liang, Meng Jiang, and Ashish Sabharwal. 2023 · 2023
Later among the works it cites.
Unsupervised Multi-document Summarization with Holistic Inference. In Findings of the Association for Computational Linguistics: IJCNLP-AACL 2023 (Findings) . 123–133
Haopeng Zhang, Sangwoo Cho, Kaiqiang Song, Xiaoyang Wang, Hongwei Wang, Jiawei Zhang, and Dong Yu. 2023a · 2023
Later among the works it cites.
Contrastive Hierarchical Discourse Graph for Scientific Document Summarization. In Proceedings of the 4th Workshop on Computational Approaches to Discourse (CODI 2023) . 37–47
Haopeng Zhang, Xiao Liu, and Jiawei Zhang. 2023c · 2023
Later among the works it cites.
DiffuSum: Generation Enhanced Extractive Summarization with Diffusion. In Findings of the Association for Computational Linguistics: ACL 2023 . 13089–13100
Haopeng Zhang, Xiao Liu, and Jiawei Zhang. 2023d · 2023
Later among the works it cites.
Extractive Summarization via ChatGPT for Faithful Summary Generation. In Findings of the Association for Computational Linguistics: EMNLP 2023 . 3270–3278
Haopeng Zhang, Xiao Liu, and Jiawei Zhang. 2023e · 2023
Later among the works it cites.
SummIt: Iterative Text Summarization via ChatGPT
Haopeng Zhang, Xiao Liu, and Jiawei Zhang. 2023f · 2023
Later among the works it cites.
Siren’s song in the AI ocean: a survey on hallucination in large language models
Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, et al · 2023
Later among the works it cites.
Fair Abstractive Summarization of Diverse Perspectives
Yusen Zhang, Nan Zhang, Yixin Liu, Alexander Fabbri, Junru Liu, Ryo Kamoi, Xiaoxin Lu, Caiming Xiong, Jieyu Zhao, Dragomir Radev, et al · 2023
Later among the works it cites.
Multi-Stage Pre-training Enhanced by ChatGPT for Multi-Scenario Multi-Domain Dialogue Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2023 . 6893–6908
Weixiao Zhou, Gengyao Li, Xianfu Cheng, Xinnian Liang, Junnan Zhu, Feifei Zhai, and Zhoujun Li. 2023 · 2023
Later among the works it cites.
A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law
Zhiyu Zoey Chen, Jing Ma, Xinlu Zhang, Nan Hao, An Yan, Armineh Nourbakhsh, Xianjun Yang, Julian McAuley, Linda Petzold, and William Yang Wang. 2024b · 2024
Closest in time.
Steffi Chern, Ethan Chern, Graham Neubig, and Pengfei Liu. 2024 · 2024
Closest in time.
Anshuman Chhabra, Hadi Askari, and Prasant Mohapatra. 2024 · 2024
Closest in time.
Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al · 2024
Closest in time.
Qlora: Efficient finetuning of quantized llms
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2024 · 2024
Closest in time.
Xue-Yong Fu, Md Tahmid Rahman Laskar, Elena Khasanova, Cheng Chen, and Shashi Bhushan TN. 2024 · 2024
Closest in time.
Analyzing the Performance of Large Language Models on Code Summarization. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) . 995–1008
Rajarshi Haldar and Julia Hockenmaier. 2024 · 2024
Closest in time.
TriSum: Learning Summarization Ability from Large Language Models with Structured Rationale
Pengcheng Jiang, Cao Xiao, Zifeng Wang, Parminder Bhatia, Jimeng Sun, and Jiawei Han. 2024 · 2024
Closest in time.
CCSUM: A large-scale and high-quality dataset for abstractive news summarization
Xiang Jiang and Markus Dreyer. 2024 · 2024
Closest in time.
The benefits, risks and bounds of personalizing the alignment of large language models to individuals
Hannah Rose Kirk, Bertie Vidgen, Paul Röttger, and Scott A Hale. 2024 · 2024
Closest in time.
Improving Faithfulness of Large Language Models in Summarization via Sliding Generation and Self-Consistency. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) . 8804–8817
Taiji Li, Zhi Li, and Yin Zhang. 2024a · 2024
Closest in time.
ISQA: Informative Factuality Feedback for Scientific Summarization
Zekai Li, Yanxia Qin, Qian Liu, and Min-Yen Kan. 2024b · 2024
Closest in time.
Unveiling the Magic: Investigating Attention Distillation in Retrieval-augmented Generation. In Proceedings of the Association for Computational Linguistics: NAACL 2024
Zizhong Li, Haopeng Zhang, and Jiawei Zhang. 2024c · 2024
Closest in time.
ChartThinker: A Contextual Chain-of-Thought Approach to Optimized Chart Summarization. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) . 3057–3074
Mengsha Liu, Daoyuan Chen, Yaliang Li, Guian Fang, and Ying Shen. 2024 · 2024
Closest in time.
Towards faithful model explanation in nlp: A survey
Qing Lyu, Marianna Apidianaki, and Chris Callison-Burch. 2024 · 2024
Closest in time.
On the Role of Summary Content Units in Text Summarization Evaluation
Marcel Nawrath, Agnieszka Nowak, Tristan Ratz, Danilo C Walenta, Juri Opitz, Leonardo FR Ribeiro, João Sedoc, Daniel Deutsch, Simon Mille, Yixin Liu, et al · 2024
Closest in time.
Automating financial reporting with natural language processing: A review and case analysis
Adedoyin Tolulope Oyewole, Omotayo Bukola Adeoye, Wilhelmina Afua Addy, Chinwe Chinazo Okoye, Onyeka Chrisanctus Ofodile, and Chinonye Esther Ugochukwu. 2024 · 2024
Closest in time.
RST-LoRA: A Discourse-Aware Low-Rank Adaptation for Long Document Abstractive Summarization
Dongqi Pu and Vera Demberg. 2024 · 2024
Closest in time.
Prompt Chaining or Stepwise Prompt? Refinement in Text Summarization
Shichao Sun, Ruifeng Yuan, Ziqiang Cao, Wenjie Li, and Pengfei Liu. 2024 · 2024
Closest in time.
TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization
Liyan Tang, Igor Shalyminov, Amy Wing-mei Wong, Jon Burnsky, Jake W Vincent, Yu’an Yang, Siffi Singh, Song Feng, Hwanjun Song, Hang Su, et al · 2024
Closest in time.
Hallucination Diversity-Aware Active Learning for Text Summarization
Yu Xia, Xu Liu, Tong Yu, Sungchul Kim, Ryan A Rossi, Anup Rao, Tung Mai, and Shuai Li. 2024 · 2024
Closest in time.
Identifying Factual Inconsistency in Summaries: Towards Effective Utilization of Large Language Model
Liyan Xu, Zhenlin Su, Mo Yu, Jin Xu, Jinho D Choi, Jie Zhou, and Fei Liu. 2024b · 2024
Closest in time.
A survey on knowledge distillation of large language models
Xiaohan Xu, Ming Li, Chongyang Tao, Tao Shen, Reynold Cheng, Jinyang Li, Can Xu, Dacheng Tao, and Tianyi Zhou. 2024a · 2024
Closest in time.
Benchmarking Large Language Models for News Summarization
Tianyi Zhang, Faisal Ladhak, Esin Durmus, Percy Liang, Kathleen McKeown, and Tatsunori Hashimoto. 2024a · 2024
Closest in time.
Benchmarking Large Language Models for News Summarization
Tianyi Zhang, Faisal Ladhak, Esin Durmus, Percy Liang, Kathleen McKeown, and Tatsunori Hashimoto. 2024b · 2024
Closest in time.
Hierarchical Attention Graph for Scientific Document Summarization in Global and Local Level
Chenlong Zhao, Xiwen Zhou, Xiaopeng Xie, and Yong Zhang. 2024 · 2024
Closest in time.
Towards a Unified Multi-Dimensional Evaluator for Text Generation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing . 2023–2038
Ming Zhong, Yang Liu, Da Yin, Yuning Mao, Yizhu Jiao, Pengfei Liu, Chenguang Zhu, Heng Ji, and Jiawei Han. 2022b · 2038
Closest in time.