# Extractive approach for text summarisation using graphs

arXiv: https://arxiv.org/abs/2106.10955 · HTML: https://mlsanity.com/paper/2106.10955/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 21 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [A Neural Attention Model for Abstractive Sentence Summarization](https://mlsanity.com/paper/1509.00685/) (2015)
- [Learning to Extract Coherent Summary via Deep Reinforcement Learning](https://mlsanity.com/paper/1804.07036/) (2018)
- [Small-world networks for summarization of biomedical articles](https://mlsanity.com/paper/1903.02861/) (2019)

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

- [Graph Neural Network and NER-Based Text Summarization](https://mlsanity.com/paper/2402.05126/) (2024)

## Abstract (arXiv)

Natural language processing is an important discipline with the aim of understanding text by its digital representation, that due to the diverse way we write and speak, is often not accurate enough. Our paper explores different graph-related algorithms that can be used in solving the text summarization problem using an extractive approach. We consider two metrics: sentence overlap and edit distance for measuring sentence similarity.