Extractive approach for text summarisation using graphs
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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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 23 references that are in the map. First posted on arXiv 21 Jun 2021.
Builds on
6 of 23 references.
References · 6
- 2017Get To The Point: Summarization with Pointer-Generator Networkscited by 1,281
- 2015Teaching Machines to Read and Comprehendcited by 1,039
- 2020Stanza: A Python Natural Language Processing Toolkit for Many Human Languagescited by 719
- 2015A Neural Attention Model for Abstractive Sentence Summarizationcited by 394
- 2018Learning to Extract Coherent Summary via Deep Reinforcement Learningcited by 63
- 2019Small-world networks for summarization of biomedical articlescited by 3
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Citing papers per year: 2024: 1.
1 paper.
- 2024Graph Neural Network and NER-Based Text Summarizationcited by 1
- 2024Graph Neural Network and NER-Based Text Summarization5 Feb 2024