# Generating Summaries for Scientific Paper Review

arXiv: https://arxiv.org/abs/2109.14059 · HTML: https://mlsanity.com/paper/2109.14059/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 29 references that are in the map.
- First posted on arXiv 28 Sep 2021.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://mlsanity.com/paper/1808.08745/) (2018)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents](https://mlsanity.com/paper/1804.05685/) (2018)
- [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://mlsanity.com/paper/2001.04063/) (2020)
- [ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks](https://mlsanity.com/paper/1909.01716/) (2019)
- [Sentence Centrality Revisited for Unsupervised Summarization](https://mlsanity.com/paper/1906.03508/) (2019)
- [What Can We Do to Improve Peer Review in NLP?](https://mlsanity.com/paper/2010.03863/) (2020)
- [Compressive Summarization with Plausibility and Salience Modeling](https://mlsanity.com/paper/2010.07886/) (2020)
- [What is this Article about? Extreme Summarization with Topic-aware Convolutional Neural Networks](https://mlsanity.com/paper/1907.08722/) (2019)
- [Overview and Results: CL-SciSumm Shared Task 2019](https://mlsanity.com/paper/1907.09854/) (2019)

## Built on by (most cited first, 1 of 1)

- [DeepProphet2 -- A Deep Learning Gene Recommendation Engine](https://mlsanity.com/paper/2208.01918/) (2022)

## 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.