mlsanityData to 7 Oct 2026

Generating Summaries for Scientific Paper Review

arXiv 2109.14059 ↗First posted 28 Sep 2021cs.CL cs.AI cs.LGcited by 1

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Abstract (arXiv)

The review process is essential to ensure the quality of publications. Recently, the increase of submissions for top venues in machine learning and NLP has caused a problem of excessive burden on reviewers and has often caused concerns regarding how this may not only overload reviewers, but also may affect the quality of the reviews. An automatic system for assisting with the reviewing process could be a solution for ameliorating the problem. In this paper, we explore automatic review summary generation for scientific papers. We posit that neural language models have the potential to be valuable candidates for this task. In order to test this hypothesis, we release a new dataset of scientific papers and their reviews, collected from papers published in the NeurIPS conference from 2013 to 2020. We evaluate state of the art neural summarization models, present initial results on the feasibility of automatic review summary generation, and propose directions for the future.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 29 references that are in the map. First posted on arXiv 28 Sep 2021.

Builds on

15 of 29 references.

References · 15

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  4. 2018Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarizationcited by 1,308
  5. 2017Get To The Point: Summarization with Pointer-Generator Networkscited by 1,281
  6. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
  7. 2016Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyondcited by 809
  8. 2018A Discourse-Aware Attention Model for Abstractive Summarization of Long Documentscited by 477
  9. 2020ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-trainingcited by 221
  10. 2019ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networkscited by 73
  11. 2019Sentence Centrality Revisited for Unsupervised Summarizationcited by 64
  12. 2020What Can We Do to Improve Peer Review in NLP?cited by 32
3 more
  1. 2020Compressive Summarization with Plausibility and Salience Modelingcited by 9
  2. 2019What is this Article about? Extreme Summarization with Topic-aware Convolutional Neural Networkscited by 8
  3. 2019Overview and Results: CL-SciSumm Shared Task 2019cited by 7

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Citing papers per year: 2022: 1.

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