作者：哈工大SCIR 冯夏冲
摘要（Summarization）是传统的自然语言处理任务之一[1]，多年以来，一直被广大研究者持续挖掘推进，该任务旨在将输入数据转换为包含关键信息的简短概述。在早些年，该方向一直以DUC，CNNDM，Gigaword等数据集为核心进行研究[2]，并取得了显著的进展。为了满足各种需求，近些年，跨语言摘要[3]，多模态摘要[4]，无监督摘要[5]，摘要事实性研究[6]，对话摘要[7]，科学文献摘要[8]，基于预训练的摘要[9]，摘要任务分析[10]等方向喷薄发展，百花齐放，论文数量持续增多，除了各大会议（例如ACL，EMNLP）中的摘要相关论文之外，arXiv也会涌现出众多摘要相关论文。
受yizhen20133868/NLP-Conferences-Code[11]，teacherpeterpan/Question-Generation-Paper-List[12]，thunlp/PLMpapers[13]，thu-coai/PaperForONLG[14]，NiuTrans/ABigSurvey[15]等项目的激励，旨在整理现有摘要研究成果，追踪最新摘要论文，中心文本生成组博士生冯夏冲收集并整理了摘要论文阅读列表，该列表每条信息包括论文题目，作者，PDF链接，论文来源，是否有实现代码，可以帮助研究者快速整合该方向核心资料，并会长期维护和迭代整理现有论文列表。
图1 摘要论文阅读列表
除论文信息之外，该仓库还包括了文本生成组摘要论文笔记与讲解PPT，可以帮助初学者快速了解与入门该任务。
图2 摘要论文笔记与讲解PPT
项目地址：
https://github.com/xcfcode/Summarization-Papers
参考资料
[1]
Paice C D. Constructing literature abstracts by computer: Techniques and prospects[J]. Inf. Process. Manag, 1990, 26(1): 171-186.
[2]
Gambhir M, Gupta V. Recent automatic text summarization techniques: a survey[J]. Artificial Intelligence Review, 2017, 47(1): 1-66.
[3]
Cao Y, Liu H, Wan X. Jointly Learning to Align and Summarize for Neural Cross-Lingual Summarization[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020: 6220-6231.
[4]
Li M, Chen X, Gao S, et al. VMSMO: Learning to Generate Multimodal Summary for Video-based News Articles[J]. arXiv preprint arXiv:2010.05406, 2020.
[5]
Kohita R, Wachi A, Zhao Y, et al. Q-learning with Language Model for Edit-based Unsupervised Summarization[J]. arXiv preprint arXiv:2010.04379, 2020.
[6]
Dong Y, Wang S, Gan Z, et al. Multi-Fact Correction in Abstractive Text Summarization[J]. arXiv preprint arXiv:2010.02443, 2020.
[7]
Feng X, Feng X, Qin B, et al. Incorporating Commonsense Knowledge into Abstractive Dialogue Summarization via Heterogeneous Graph Networks[J]. arXiv preprint arXiv:2010.10044, 2020.
[8]
Subramanian S, Li R, Pilault J, et al. On extractive and abstractive neural document summarization with transformer language models[J]. arXiv preprint arXiv:1909.03186, 2019.
[9]
Bi B, Li C, Wu C, et al. PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation[J]. arXiv preprint arXiv:2004.07159, 2020.
[10]
Bhandari M, Gour P, Ashfaq A, et al. Re-evaluating Evaluation in Text Summarization[J]. arXiv preprint arXiv:2010.07100, 2020.
[11]
https://github.com/yizhen20133868/NLP-Conferences-Code
[12]
https://github.com/teacherpeterpan/Question-Generation-Paper-List
[13]
https://github.com/thunlp/PLMpapers
[14]
https://github.com/thu-coai/PaperForONLG
[15]
