Fetching the paper…
Reading the bibliography…
Automatically summarizing large text collections is a valuable tool for document research, with applications in journalism, academic research, legal work, and many other fields.
Topic detection and tracking pilot study final report
James Allan, Jaime G. Carbonell, George R. Doddington, Jonathan Yamron, and Yiming Yang. 1998 · 1998
Earlier work this paper cites.
Query Based Event Extraction along a Timeline
Hai Leong Chieu and Yoong Keok Lee. 2004 · 2004
Earlier work this paper cites.
ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
Evaluating content selection in summarization: The pyramid method
Ani Nenkova and Rebecca Passonneau. 2004 · 2004
Earlier work this paper cites.
Overview of the TAC 2008 Update Summarization Task
Hoa Dang and Karolina Owczarzak. 2009 · 2008
Earlier work this paper cites.
Predicting relevant news events for timeline summaries
Giang Binh Tran, Mohammad Alrifai, and Dat Quoc Nguyen. 2013 · 2013
Earlier work this paper cites.
TREC 2015 Temporal Summarization Track Overview
Javed Aslam, Fernando Diaz, Matthew Ekstrand-Abueg, Richard McCreadie, Virgil Pavlu, and Tetsuya Sakai. 2015 · 2015
Earlier work this paper cites.
Timeline summarization from relevant headlines
Giang Tran, Mohammad Alrifai, and Eelco Herder. 2015 · 2015
Earlier work this paper cites.
Socially-informed timeline generation for complex events
Lu Wang, Claire Cardie, and Galen Marchetti. 2015 · 2015
Earlier work this paper cites.
Best-worst scaling more reliable than rating scales: A case study on sentiment intensity annotation
Svetlana Kiritchenko and Saif Mohammad. 2017 · 2017
Earlier work this paper cites.
Crowdsourcing lightweight pyramids for manual summary evaluation
Ori Shapira, David Gabay, Yang Gao, Hadar Ronen, Ramakanth Pasunuru, Mohit Bansal, Yael Amsterdamer, and Ido Dagan. 2019 · 2019
Earlier work this paper cites.
A large-scale multi-document summarization dataset from the Wikipedia current events portal
Demian Gholipour Ghalandari, Chris Hokamp, Nghia The Pham, John Glover, and Georgiana Ifrim. 2020 · 2020
Earlier work this paper cites.
Roformer: Enhanced transformer with rotary position embedding
Jianlin Su, Yu Lu, Shengfeng Pan, Ahmed Murtadha, Bo Wen, and Yunfeng Liu. 2021 · 2021
Earlier work this paper cites.
News summarization and evaluation in the era of gpt-3
Tanya Goyal, Junyi Jessy Li, and Greg Durrett. 2022 · 2022
Cited alongside, same era.
Exploring neural models for query-focused summarization
Jesse Vig, Alexander Fabbri, Wojciech Kryscinski, Chien-Sheng Wu, and Wenhao Liu. 2022 · 2022
Cited alongside, same era.
Needle in a haystack - pressure testing llms
Greg Kamradt. 2023 · 2023
Cited alongside, same era.
Efficient memory management for large language model serving with pagedattention
Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica. 2023 · 2023
Cited alongside, same era.
Towards interpretable and efficient automatic reference-based summarization evaluation
Yixin Liu, Alexander Fabbri, Yilun Zhao, Pengfei Liu, Shafiq Joty, Chien-Sheng Wu, Caiming Xiong, and Dragomir Radev. 2023b · 2023
Cited alongside, same era.
One thousand and one pairs: A "novel" challenge for long-context language models
Marzena Karpinska, Katherine Thai, Kyle Lo, Tanya Goyal, and Mohit Iyyer. 2024 · 2024
Later among the works it cites.
FABLES: Evaluating faithfulness and content selection in book-length summarization
Yekyung Kim, Yapei Chang, Marzena Karpinska, Aparna Garimella, Varun Manjunatha, Kyle Lo, Tanya Goyal, and Mohit Iyyer. 2024 · 2024
Later among the works it cites.
Summary of a haystack: A challenge to long-context llms and rag systems
Philippe Laban, Alexander R. Fabbri, Caiming Xiong, and Chien-Sheng Wu. 2024 · 2024
Later among the works it cites.
A controlled study on long context extension and generalization in llms
Yi Lu, Jing Nathan Yan, Songlin Yang, Justin T. Chiu, Siyu Ren, Fei Yuan, Wenting Zhao, Zhiyong Wu, and Alexander M. Rush. 2024 · 2024
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Bowen Peng, Jeffrey Quesnelle, Honglu Fan, and Enrico Shippole. 2023 · 2023
Cited alongside, same era.
Background summarization of event timelines
Adithya Pratapa, Kevin Small, and Markus Dreyer. 2023 · 2023
Cited alongside, same era.
Booookscore: A systematic exploration of book-length summarization in the era of LLMs
Yapei Chang, Kyle Lo, Tanya Goyal, and Mohit Iyyer. 2024 · 2024
Cited alongside, same era.
c4ai-command-r-08-2024
Cohere For AI. 2024 · 2024
Cited alongside, same era.
LongroPE: Extending LLM context window beyond 2 million tokens
Yiran Ding, Li Lyna Zhang, Chengruidong Zhang, Yuanyuan Xu, Ning Shang, Jiahang Xu, Fan Yang, and Mao Yang. 2024 · 2024
Cited alongside, same era.
RULER: What’s the real context size of your long-context language models?
Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman, Shantanu Acharya, Dima Rekesh, Fei Jia, and Boris Ginsburg. 2024 · 2024
Cited alongside, same era.
Embrace divergence for richer insights: A multi-document summarization benchmark and a case study on summarizing diverse information from news articles
Kung-Hsiang Huang, Philippe Laban, Alexander Fabbri, Prafulla Kumar Choubey, Shafiq Joty, Caiming Xiong, and Chien-Sheng Wu. 2024 · 2024
Cited alongside, same era.
Rui Meng*, Ye Liu*, Shafiq Rayhan Joty, Caiming Xiong, Yingbo Zhou, and Semih Yavuz. 2024 · 2024
Later among the works it cites.
Llama 3.1 model card
Meta. 2024 · 2024
Later among the works it cites.
On context utilization in summarization with large language models
Mathieu Ravaut, Aixin Sun, Nancy Chen, and Shafiq Joty. 2024 · 2024
Later among the works it cites.
RAPTOR: Recursive abstractive processing for tree-organized retrieval
Parth Sarthi, Salman Abdullah, Aditi Tuli, Shubh Khanna, Anna Goldie, and Christopher D Manning. 2024 · 2024
Later among the works it cites.
Jamba-1.5: Hybrid transformer-mamba models at scale
Jamba Team, Barak Lenz, Alan Arazi, Amir Bergman, Avshalom Manevich, Barak Peleg, Ben Aviram, Chen Almagor, Clara Fridman, Dan Padnos, Daniel Gissin, Daniel Jannai, Dor Muhlgay, Dor Zimberg, Edden M Gerber, Elad Dolev, Eran Krakovsky, Erez Safahi, Erez Schwartz, Gal Cohen, Gal Shachaf, Haim Rozenblum, Hofit Bata, Ido Blass, Inbal Magar, Itay Dalmedigos, Jhonathan Osin, Julie Fadlon, Maria Rozman, Matan Danos, Michael Gokhman, Mor Zusman, Naama Gidron, Nir Ratner, Noam Gat, Noam Rozen, Oded Fried, Ohad Leshno, Omer Antverg, Omri Abend, Opher Lieber, Or Dagan, Orit Cohavi, Raz Alon, Ro’i Belson, Roi Cohen, Rom Gilad, Roman Glozman, Shahar Lev, Shaked Meirom, Tal Delbari, Tal Ness, Tomer Asida, Tom Ben Gal, Tom Braude, Uriya Pumerantz, Yehoshua Cohen, Yonatan Belinkov, Yuval Globerson, Yuval Peleg Levy, and Yoav Shoham. 2024 · 2024
Later among the works it cites.
RECOMP: Improving retrieval-augmented LMs with context compression and selective augmentation
Fangyuan Xu, Weijia Shi, and Eunsol Choi. 2024 · 2024
Later among the works it cites.
In defense of rag in the era of long-context language models
Tan Yu, Anbang Xu, and Rama Akkiraju. 2024 · 2024
Later among the works it cites.
Longembed: Extending embedding models for long context retrieval
Dawei Zhu, Liang Wang, Nan Yang, Yifan Song, Wenhao Wu, Furu Wei, and Sujian Li. 2024 · 2024
Later among the works it cites.