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--- nlp:summarization [2021/04/02 05:50] – jmflanig
+++ nlp:summarization [2025/05/14 18:33] (current) – [Overviews] jmflanig
@@ Line 2: / Line 2: @@
 ===== Overviews =====
-Best overviews: {{papers:Klymenko_2020_Automatic_Text_Summarization.pdf|Klymenko & Braun 2020 - Automatic Text Summarization: A State-of-the-Art Review}} and {{papers:el-kassas_2021_automatic_text_summarization.pdf|El-Kassas et al 2021 - Automatic Text Summarization: A Comprehensive Survey}}
+Best overviews (as of 2021): {{papers:Klymenko_2020_Automatic_Text_Summarization.pdf|Klymenko & Braun 2020 - Automatic Text Summarization: A State-of-the-Art Review}} and {{papers:el-kassas_2021_automatic_text_summarization.pdf|El-Kassas et al 2021 - Automatic Text Summarization: A Comprehensive Survey}}
-  * General
+  * **General**
     * [[https://arxiv.org/pdf/1707.02268.pdf|Allahyari et al 2017 - Text Summarization Techniques: A Brief Survey]]
     * [[http://jad.shahroodut.ac.ir/article_1189_28715967fcd8b7bfb463ab90aca5a9f7.pdf|Nazari & Mahdavi 2019 - A survey on Automatic Text Summarization]] Kind of a weird survey
@@ Line 10: / Line 10: @@
     * {{papers:pramita_2020_review_of_automatic.pdf|Widyassari et al 2020 - Review of Automatic Text Summarization Techniques & Methods}} Not the best review: I can't believe they didn't include Rush et al 2015.
     * **{{papers:el-kassas_2021_automatic_text_summarization.pdf|El-Kassas et al 2021 - Automatic Text Summarization: A Comprehensive Survey}}**
-  * Abstractive
+  * **Abstractive**
     * [[https://www.worldscientific.com/doi/abs/10.1142/9789813274884_0006|Baumel & Elhadad 2019 - A Survey of Neural Models for Abstractive Summarization]]
     * **{{papers:lin_2019_abstractive_summarization.pdf|Lin & Ng 2019 - Abstractive Summarization: A Survey of the State of the Art}}**
     * [[https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9328413|Syed et al 2021 - A Survey of the State-of-the-Art Models in Neural Abstractive Text Summarization]]
-  * Multi-document
+  * **Multi-document**
     * [[https://arxiv.org/pdf/2011.04843.pdf|Ma et al 2020 - Multi-document Summarization via Deep Learning Techniques: A Survey]]
-  * Older surveys
+  * **Datasets**
+    * [[https://arxiv.org/pdf/2411.04585|Dahan & Stanovsky 2024 - The State and Fate of Summarization Datasets: A Survey]]
+  * **Older surveys**
     * [[https://www.cs.cmu.edu/~afm/Home_files/Das_Martins_survey_summarization.pdf|Das & Martins 2009 - A Survey on Automatic Text Summarization]]
 ===== Papers =====
+  * [[https://arxiv.org/pdf/1509.00685.pdf|Rush et al 2015 - A Neural Attention Model for Abstractive Sentence Summarization]]
+  * [[https://arxiv.org/pdf/1602.06023.pdf|Nallapati et al 2016 - Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond]]
+  * [[https://arxiv.org/pdf/1704.04368.pdf|See et al 2017 - Get To The Point: Summarization with Pointer-Generator Networks]]
   * [[https://arxiv.org/pdf/1904.02321.pdf|Arumae & Liu 2018 - Guiding Extractive Summarization with Question-Answering Rewards]]
+  * [[https://www.aclweb.org/anthology/2021.eacl-main.213.pdf|Padmakumar & He 2021 - Unsupervised Extractive Summarization using Pointwise Mutual Information]]
 ===== Multi-Document =====
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 ===== Datasets =====
+  * **Surveys**
+    * [[https://arxiv.org/pdf/2411.04585|Dahan & Stanovsky 2024 - The State and Fate of Summarization Datasets: A Survey]]
+  * CNN / Daily Mail summarization dataset
+    * Paper: [[https://arxiv.org/pdf/1602.06023.pdf|Nallapati et al 2016 - Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond]]
+    * Processed Dataset: [[https://github.com/harvardnlp/sent-summary|here]] or [[https://github.com/abisee/cnn-dailymail|here]] or [[https://github.com/JafferWilson/Process-Data-of-CNN-DailyMail|here]], Original Dataset: [[https://github.com/alesee/abstractive-text-summarization|github]]
   * [[https://arxiv.org/pdf/1804.11283.pdf|Grusky et al 2018 - NEWSROOM: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies]]
   * [[https://arxiv.org/pdf/2010.03093.pdf|Ladhak et al 2020 - WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization]]
+  * Medical Domain
+    * [[https://www.aclweb.org/anthology/2021.naacl-main.395.pdf|Devaraj et al 2021 - Paragraph-level Simplification of Medical Texts]] (Actually a [[text simplification]] task) Dataset size: 3,500 training, 400 dev, 400 test
 ===== Evaluation =====
@@ Line 38: / Line 50: @@
   * [[https://scholar.google.com/citations?user=LIjnUGgAAAAJ&hl=en|Alexander Rush]]
   * [[https://scholar.google.com/citations?user=Is0pLz0AAAAJ&hl=en|Michael Elhadad]]
+  * [[https://scholar.google.com/citations?user=uczqEdUAAAAJ&hl=en|Lu Wang]]
+===== Related Pages ======
+  * [[Keyphrase Generation]]
+  * [[Text Simplification]]