Best overviews: Klymenko & Braun 2020 - Automatic Text Summarization: A State-of-the-Art Review and El-Kassas et al 2021 - Automatic Text Summarization: A Comprehensive Survey

• General
  • Allahyari et al 2017 - Text Summarization Techniques: A Brief Survey
  • Nazari & Mahdavi 2019 - A survey on Automatic Text Summarization Kind of a weird survey
  • Klymenko & Braun 2020 - Automatic Text Summarization: A State-of-the-Art Review Good overview of the work in recent years.
  • 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.
  • El-Kassas et al 2021 - Automatic Text Summarization: A Comprehensive Survey
• Abstractive
  • Baumel & Elhadad 2019 - A Survey of Neural Models for Abstractive Summarization
  • Lin & Ng 2019 - Abstractive Summarization: A Survey of the State of the Art
  • Syed et al 2021 - A Survey of the State-of-the-Art Models in Neural Abstractive Text Summarization
• Multi-document
  • Ma et al 2020 - Multi-document Summarization via Deep Learning Techniques: A Survey
• Older surveys
  • Das & Martins 2009 - A Survey on Automatic Text Summarization

• Rush et al 2015 - A Neural Attention Model for Abstractive Sentence Summarization
• Nallapati et al 2016 - Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond
• See et al 2017 - Get To The Point: Summarization with Pointer-Generator Networks
• Arumae & Liu 2018 - Guiding Extractive Summarization with Question-Answering Rewards
• Padmakumar & He 2021 - Unsupervised Extractive Summarization using Pointwise Mutual Information

• Lebanoff et al 2018 - Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization

• CNN / Daily Mail summarization dataset
  • Paper: Nallapati et al 2016 - Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond
  • Processed Dataset: here or here or here, Original Dataset: github
• Grusky et al 2018 - NEWSROOM: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies
• Ladhak et al 2020 - WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization
• Medical Domain
  • Devaraj et al 2021 - Paragraph-level Simplification of Medical Texts (Actually a text simplification task) Dataset size: 3,500 training, 400 dev, 400 test

• Eyal et al 2019 - Question Answering as an Automatic Evaluation Metric for News Article Summarization

• Fei Liu
• Alexander Rush
• Michael Elhadad
• Lu Wang

• Keyphrase Generation