文字摘要論文列表

語言: CN / TW / HK

摘要(Summarization)是傳統的自然語言處理任務之一 [1] ,多年以來,一直被廣大研究者持續挖掘推進,該任務旨在將輸入資料轉換為包含關鍵資訊的簡短概述。在早些年,該方向一直以DUC,CNNDM,Gigaword等資料集為核心進行研究 [2] ,並取得了顯著的進展。為了滿足各種需求,近些年,跨語言摘要 [3] ,多模態摘要 [4] ,無監督摘要 [5] ,摘要事實性研究 [6] ,對話摘要 [7] ,科學文獻摘要 [8] ,基於預訓練的摘要 [9] ,摘要任務分析 [10] 等方向噴薄發展,百花齊放,論文數量持續增多,除了各大會議(例如ACL,EMNLP)中的摘要相關論文之外,arXiv也會湧現出眾多摘要相關論文。

受yizhen20133868/NLP-Conferences-Code [11] ,teacherpeterpan/Question-Generation-Paper-List [12] ,thunlp/PLMpapers [13] ,thu-coai/PaperForONLG [14] ,NiuTrans/ABigSurvey [15] 等專案的激勵,旨在整理現有摘要研究成果,追蹤最新摘要論文,中心文字生成組 博士生馮夏衝 收集並整理了摘要論文閱讀列表,該列表每條資訊包括論文題目,作者,PDF連結,論文來源,是否有實現程式碼,可以幫助研究者快速整合該方向核心資料,並會長期維護和迭代整理現有論文列表。

圖1 摘要論文閱讀列表
除論文資訊之外,該倉庫還包括了文字生成組摘要論文筆記與講解PPT,可以幫助初學者快速瞭解與入門該任務。
圖2 摘要論文筆記與講解PPT
專案地址:

https://github.com/xcfcode/Summarization-Papers

參考資料

[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]

https://github.com/NiuTrans

本期責任編輯:李忠陽

本期編輯:朱文軒

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