# Data-driven Summarization of Scientific Articles

arXiv: https://arxiv.org/abs/1804.08875 · HTML: https://mlsanity.com/paper/1804.08875/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2018.

## Built on by (most cited first, 8 of 8)

- [Controllable Abstractive Dialogue Summarization with Sketch Supervision](https://mlsanity.com/paper/2105.14064/) (2021)
- [Topic-Aware Contrastive Learning for Abstractive Dialogue Summarization](https://mlsanity.com/paper/2109.04994/) (2021)
- [Generation of Highlights from Research Papers Using Pointer-Generator Networks and SciBERT Embeddings](https://mlsanity.com/paper/2302.07729/) (2023)
- [FREDSum: A Dialogue Summarization Corpus for French Political Debates](https://mlsanity.com/paper/2312.04843/) (2023)
- [Topic Segmentation of Research Article Collections](https://mlsanity.com/paper/2205.11249/) (2022)
- [SimCSum: Joint Learning of Simplification and Cross-lingual Summarization for Cross-lingual Science Journalism](https://mlsanity.com/paper/2304.01621/) (2023)
- [AlbNews: A Corpus of Headlines for Topic Modeling in Albanian](https://mlsanity.com/paper/2402.04028/) (2024)
- [Automatic Text Summarization (ATS) for Research Documents in Sorani Kurdish](https://mlsanity.com/paper/2504.14630/) (2025)

## Abstract (arXiv)

Data-driven approaches to sequence-to-sequence modelling have been successfully applied to short text summarization of news articles. Such models are typically trained on input-summary pairs consisting of only a single or a few sentences, partially due to limited availability of multi-sentence training data. Here, we propose to use scientific articles as a new milestone for text summarization: large-scale training data come almost for free with two types of high-quality summaries at different levels - the title and the abstract. We generate two novel multi-sentence summarization datasets from scientific articles and test the suitability of a wide range of existing extractive and abstractive neural network-based summarization approaches. Our analysis demonstrates that scientific papers are suitable for data-driven text summarization. Our results could serve as valuable benchmarks for scaling sequence-to-sequence models to very long sequences.