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The ever-increasing volume of digital information necessitates efficient methods for users to extract key insights from lengthy documents.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
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Lexrank: Graph-based lexical centrality as salience in text summarization
Günes Erkan and Dragomir R Radev · 2004
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
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Textrank: Bringing order into text
Rada Mihalcea and Paul Tarau · 2004
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Meteor: An automatic metric for mt evaluation with improved correlation with human judgments
Satanjeev Banerjee and Alon Lavie · 2005
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A survey automatic text summarization
Oguzhan Tas and Farzad Kiyani · 2007
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Abstractive sentence summarization with attentive recurrent neural networks
Sumit Chopra, Michael Auli, and Alexander M Rush · 2016
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Aspect level sentiment classification with deep memory network
Duyu Tang, Bing Qin, and Ting Liu · 2016
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Attention-based lstm for aspect-level sentiment classification
Yequan Wang, Minlie Huang, Xiaoyan Zhu, and Li Zhao · 2016
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Aspect based sentiment oriented summarization of hotel reviews
Nadeem Akhtar, Nashez Zubair, Abhishek Kumar, and Tameem Ahmad · 2017
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Recent automatic text summarization techniques: a survey
Mahak Gambhir and Vishal Gupta · 2017
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Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J Liu, and Christopher D Manning · 2017
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Aspect-based summarization of pros and cons in unstructured product reviews
Florian Kunneman, Sander Wubben, Antal van den Bosch, and Emiel Krahmer · 2018
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Unsupervised aspect-based multi-document abstractive summarization
Maximin Coavoux, Hady Elsahar, and Matthias Gallé · 2019
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Inducing document structure for aspect-based summarization
Lea Frermann and Alexandre Klementiev · 2019
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Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer · 2019
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Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi · 2019
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Longformer: The long-document transformer
Iz Beltagy, Matthew E Peters, and Arman Cohan · 2020
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Tldr: Extreme summarization of scientific documents
Isabel Cachola, Kyle Lo, Arman Cohan, and Daniel S Weld · 2020
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Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer · 2020
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Aspect-aware multimodal summarization for chinese e-commerce products
Haoran Li, Peng Yuan, Song Xu, Youzheng Wu, Xiaodong He, and Bowen Zhou · 2020
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Read what you need: Controllable aspect-based opinion summarization of tourist reviews
Rajdeep Mukherjee, Hari Chandana Peruri, Uppada Vishnu, Pawan Goyal, Sourangshu Bhattacharya, and Niloy Ganguly · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
Parameter-efficient fine-tuning of large-scale pre-trained language models
Ning Ding, Yujia Qin, Guang Yang, Fuchao Wei, Zonghan Yang, Yusheng Su, Shengding Hu, Yulin Chen, Chi-Min Chan, Weize Chen, et al · 2023
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On the effectiveness of parameter-efficient fine-tuning
Zihao Fu, Haoran Yang, Anthony Man-Cho So, Wai Lam, Lidong Bing, and Nigel Collier · 2023
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Allure: Auditing and improving llm-based evaluation of text using iterative in-context-learning
Hosein Hasanbeig, Hiteshi Sharma, Leo Betthauser, Felipe Vieira Frujeri, and Ida Momennejad · 2023
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Can large language models explain themselves? a study of llm-generated self-explanations
Shiyuan Huang, Siddarth Mamidanna, Shreedhar Jangam, Yilun Zhou, and Leilani H Gilpin · 2023
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Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al · 2023
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Pegasus: Pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter Liu · 2020
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Automatic text summarization: A comprehensive survey
Wafaa S El-Kassas, Cherif R Salama, Ahmed A Rafea, and Hoda K Mohamed · 2021
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Wikiasp: A dataset for multi-domain aspect-based summarization
Hiroaki Hayashi, Prashant Budania, Peng Wang, Chris Ackerson, Raj Neervannan, and Graham Neubig · 2021
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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al · 2022
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Longt5: Efficient text-to-text transformer for long sequences
Mandy Guo, Joshua Ainslie, David C Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, and Yinfei Yang · 2022
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Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al · 2022
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Large language models can self-improve
Jiaxin Huang, Shixiang Shane Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, and Jiawei Han · 2022
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Calibrating llm-based evaluator
Yuxuan Liu, Tianchi Yang, Shaohan Huang, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, and Qi Zhang · 2023
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Guilherme Penedo, Quentin Malartic, Daniel Hesslow, Ruxandra Cojocaru, Alessandro Cappelli, Hamza Alobeidli, Baptiste Pannier, Ebtesam Almazrouei, and Julien Launay · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Can large language models really improve by self-critiquing their own plans?
Karthik Valmeekam, Matthew Marquez, and Subbarao Kambhampati · 2023
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Exploring the limits of chatgpt for query or aspect-based text summarization
Xianjun Yang, Yan Li, Xinlu Zhang, Haifeng Chen, and Wei Cheng · 2023
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Balancing specialized and general skills in llms: The impact of modern tuning and data strategy
Zheng Zhang, Chen Zheng, Da Tang, Ke Sun, Yukun Ma, Yingtong Bu, Xun Zhou, and Liang Zhao · 2023
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Don’t make your llm an evaluation benchmark cheater
Kun Zhou, Yutao Zhu, Zhipeng Chen, Wentong Chen, Wayne Xin Zhao, Xu Chen, Yankai Lin, Ji-Rong Wen, and Jiawei Han · 2023
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Llm-based nlg evaluation: Current status and challenges
Mingqi Gao, Xinyu Hu, Jie Ruan, Xiao Pu, and Xiaojun Wan · 2024
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Shichao Sun, Junlong Li, Weizhe Yuan, Ruifeng Yuan, Wenjie Li, and Pengfei Liu · 2024
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Gemma: Open models based on gemini research and technology
Gemma Team, Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, et al · 2024
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Aya model: An instruction finetuned open-access multilingual language model
Ahmet Üstün, Viraat Aryabumi, Zheng-Xin Yong, Wei-Yin Ko, Daniel D’souza, Gbemileke Onilude, Neel Bhandari, Shivalika Singh, Hui-Lee Ooi, Amr Kayid, et al · 2024
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Unveiling the generalization power of fine-tuned large language models
Haoran Yang, Yumeng Zhang, Jiaqi Xu, Hongyuan Lu, Pheng Ann Heng, and Wai Lam · 2024
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