# A Supervised Approach to Extractive Summarisation of Scientific Papers

arXiv: https://arxiv.org/abs/1706.03946 · HTML: https://mlsanity.com/paper/1706.03946/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2017.

## Built on by (most cited first, 30 of 33)

- [Recursively Summarizing Books with Human Feedback](https://mlsanity.com/paper/2109.10862/) (2021)
- [Can large language models provide useful feedback on research papers? A large-scale empirical analysis](https://mlsanity.com/paper/2310.01783/) (2023)
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- [Generating Scientific Claims for Zero-Shot Scientific Fact Checking](https://mlsanity.com/paper/2203.12990/) (2022)
- [Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents](https://mlsanity.com/paper/2106.00130/) (2021)
- [SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis](https://mlsanity.com/paper/2301.09201/) (2023)
- [CiteWorth: Cite-Worthiness Detection for Improved Scientific Document Understanding](https://mlsanity.com/paper/2105.10912/) (2021)
- [LimGen: Probing the LLMs for Generating Suggestive Limitations of Research Papers](https://mlsanity.com/paper/2403.15529/) (2024)
- [X-SCITLDR: Cross-Lingual Extreme Summarization of Scholarly Documents](https://mlsanity.com/paper/2205.15051/) (2022)
- [Generation of Highlights from Research Papers Using Pointer-Generator Networks and SciBERT Embeddings](https://mlsanity.com/paper/2302.07729/) (2023)
- [Named Entity Recognition Based Automatic Generation of Research Highlights](https://mlsanity.com/paper/2303.12795/) (2023)
- [GoSum: Extractive Summarization of Long Documents by Reinforcement Learning and Graph Organized discourse state](https://mlsanity.com/paper/2211.10247/) (2022)
- [Contrastive Hierarchical Discourse Graph for Scientific Document Summarization](https://mlsanity.com/paper/2306.00177/) (2023)
- [LawSum: A weakly supervised approach for Indian Legal Document Summarization](https://mlsanity.com/paper/2110.01188/) (2021)
- [Summaformers @ LaySumm 20, LongSumm 20](https://mlsanity.com/paper/2101.03553/) (2021)
- [Determining the Credibility of Science Communication](https://mlsanity.com/paper/2105.14473/) (2021)
- [Citance-Contextualized Summarization of Scientific Papers](https://mlsanity.com/paper/2311.02408/) (2023)
- [Sequence-Based Extractive Summarisation for Scientific Articles](https://mlsanity.com/paper/2204.03301/) (2022)
- [Can pre-trained language models generate titles for research papers?](https://mlsanity.com/paper/2409.14602/) (2024)
- [The State and Fate of Summarization Datasets: A Survey](https://mlsanity.com/paper/2411.04585/) (2024)
- [NUTSHELL: A Dataset for Abstract Generation from Scientific Talks](https://mlsanity.com/paper/2502.16942/) (2025)
- [SuMe: A Dataset Towards Summarizing Biomedical Mechanisms](https://mlsanity.com/paper/2205.04652/) (2022)
- [How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning](https://mlsanity.com/paper/2501.15398/) (2025)
- [Lay Text Summarisation Using Natural Language Processing: A Narrative Literature Review](https://mlsanity.com/paper/2303.14222/) (2023)
- [Introducing Spotlight: A Novel Approach for Generating Captivating Key Information from Documents](https://mlsanity.com/paper/2509.10935/) (2025)
- [A Comprehensive Attempt to Research Statement Generation](https://mlsanity.com/paper/2104.14339/) (2021)
- [AI based Presentation Creator With Customized Audio Content Delivery](https://mlsanity.com/paper/2106.14213/) (2021)
- [Community-Driven Comprehensive Scientific Paper Summarization: Insight from cvpaper.challenge](https://mlsanity.com/paper/2203.09109/) (2022)
- [FinRED: A Dataset for Relation Extraction in Financial Domain](https://mlsanity.com/paper/2306.03736/) (2023)
- [Overview of the SciHigh Track at FIRE 2025: Research Highlight Generation from Scientific Papers](https://mlsanity.com/paper/2601.11582/) (2026)

## Abstract (arXiv)

Automatic summarisation is a popular approach to reduce a document to its main arguments. Recent research in the area has focused on neural approaches to summarisation, which can be very data-hungry. However, few large datasets exist and none for the traditionally popular domain of scientific publications, which opens up challenging research avenues centered on encoding large, complex documents. In this paper, we introduce a new dataset for summarisation of computer science publications by exploiting a large resource of author provided summaries and show straightforward ways of extending it further. We develop models on the dataset making use of both neural sentence encoding and traditionally used summarisation features and show that models which encode sentences as well as their local and global context perform best, significantly outperforming well-established baseline methods.