# Scientific Article Summarization Using Citation-Context and Article's Discourse Structure

arXiv: https://arxiv.org/abs/1704.06619 · HTML: https://mlsanity.com/paper/1704.06619/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2017.

## Built on by (most cited first, 17 of 17)

- [Can We Automate Scientific Reviewing?](https://mlsanity.com/paper/2102.00176/) (2021)
- [MultiCite: Modeling realistic citations requires moving beyond the single-sentence single-label setting](https://mlsanity.com/paper/2107.00414/) (2021)
- [CiteSum: Citation Text-guided Scientific Extreme Summarization and Domain Adaptation with Limited Supervision](https://mlsanity.com/paper/2205.06207/) (2022)
- [A Guide To Effectively Leveraging LLMs for Low-Resource Text Summarization: Data Augmentation and Semi-supervised Approaches](https://mlsanity.com/paper/2407.07341/) (2024)
- [When Large Language Models Meet Citation: A Survey](https://mlsanity.com/paper/2309.09727/) (2023)
- [Scientific Paper Extractive Summarization Enhanced by Citation Graphs](https://mlsanity.com/paper/2212.04214/) (2022)
- [Knowledge-Centric Templatic Views of Documents](https://mlsanity.com/paper/2401.06945/) (2024)
- [TSTR: Too Short to Represent, Summarize with Details! Intro-Guided Extended Summary Generation](https://mlsanity.com/paper/2206.00847/) (2022)
- [Citance-Contextualized Summarization of Scientific Papers](https://mlsanity.com/paper/2311.02408/) (2023)
- [KG-CTG: Citation Generation through Knowledge Graph-guided Large Language Models](https://mlsanity.com/paper/2404.09763/) (2024)
- [Cross-Lingual Citations in English Papers: A Large-Scale Analysis of Prevalence, Usage, and Impact](https://mlsanity.com/paper/2111.05097/) (2021)
- [CiteFusion: An Ensemble Framework for Citation Intent Classification Harnessing Dual-Model Binary Couples and SHAP Analyses](https://mlsanity.com/paper/2407.13329/) (2024)
- [SCIRGC: Multi-Granularity Citation Recommendation and Citation Sentence Preference Alignment](https://mlsanity.com/paper/2505.20103/) (2025)
- [Accelerating Scientific Discovery with Multi-Document Summarization of Impact-Ranked Papers](https://mlsanity.com/paper/2508.03962/) (2025)
- [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)
- [InsightGUIDE: An Opinionated AI Assistant for Guided Critical Reading of Scientific Literature](https://mlsanity.com/paper/2509.20493/) (2025)

## Abstract (arXiv)

We propose a summarization approach for scientific articles which takes advantage of citation-context and the document discourse model. While citations have been previously used in generating scientific summaries, they lack the related context from the referenced article and therefore do not accurately reflect the article's content. Our method overcomes the problem of inconsistency between the citation summary and the article's content by providing context for each citation. We also leverage the inherent scientific article's discourse for producing better summaries. We show that our proposed method effectively improves over existing summarization approaches (greater than 30% improvement over the best performing baseline) in terms of textscRouge scores on TAC2014 scientific summarization dataset. While the dataset we use for evaluation is in the biomedical domain, most of our approaches are general and therefore adaptable to other domains.