# Generating Extractive Summaries of Scientific Paradigms

arXiv: https://arxiv.org/abs/1402.0556 · HTML: https://mlsanity.com/paper/1402.0556/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2014.

## Built on by (most cited first, 7 of 7)

- [Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health](https://mlsanity.com/paper/2306.10070/) (2023)
- [CiteSum: Citation Text-guided Scientific Extreme Summarization and Domain Adaptation with Limited Supervision](https://mlsanity.com/paper/2205.06207/) (2022)
- [Automatic Related Work Generation: A Meta Study](https://mlsanity.com/paper/2201.01880/) (2022)
- [Nutri-bullets: Summarizing Health Studies by Composing Segments](https://mlsanity.com/paper/2103.11921/) (2021)
- [A Comprehensive Attempt to Research Statement Generation](https://mlsanity.com/paper/2104.14339/) (2021)
- [Who should I Collaborate with? A Comparative Study of Academia and Industry Research Collaboration in NLP](https://mlsanity.com/paper/2308.04524/) (2023)
- [Transformer-Based Language Models Across Domain Verticals: Architectures, Applications and Critical Assessment](https://mlsanity.com/paper/2606.24331/) (2026)

## Abstract (arXiv)

Researchers and scientists increasingly find themselves in the position of having to quickly understand large amounts of technical material. Our goal is to effectively serve this need by using bibliometric text mining and summarization techniques to generate summaries of scientific literature. We show how we can use citations to produce automatically generated, readily consumable, technical extractive summaries. We first propose C-LexRank, a model for summarizing single scientific articles based on citations, which employs community detection and extracts salient information-rich sentences. Next, we further extend our experiments to summarize a set of papers, which cover the same scientific topic. We generate extractive summaries of a set of Question Answering (QA) and Dependency Parsing (DP) papers, their abstracts, and their citation sentences and show that citations have unique information amenable to creating a summary.