Extractive summarization using semigraph (ESSg)
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DOI: 10.1007/s12530-018-9246-8
Abstract: Summary is the meaningful concise version of a text document. Generally existing statistical, knowledge based and discourse based extractive summarization methods use sentence similarity to extract informative sentences. This paper presents an innovative application of… read more here.
Keywords: text document; sentence; extractive summarization; using semigraph ... See more keywords
Learning-Free Unsupervised Extractive Summarization Model
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DOI: 10.1109/access.2021.3051237
Abstract: Text summarization is an information condensation technique that abbreviates a source document to a few representative sentences with the intention to create a coherent summary containing relevant information of source corpora. This promising subject has… read more here.
Keywords: learning free; unsupervised extractive; model; extractive summarization ... See more keywords
Greedy Optimization Method for Extractive Summarization of Scientific Articles
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DOI: 10.1109/access.2021.3136302
Abstract: This work presents a method for summarizing scientific articles from the arXive and PubMed datasets using a greedy Extractive Summarization algorithm. We used the approach along with Variable Neighborhood Search (VNS) to learn what is… read more here.
Keywords: optimization method; scientific articles; method; greedy optimization ... See more keywords
N-GPETS: Neural Attention Graph-Based Pretrained Statistical Model for Extractive Text Summarization
Sign Up to like & getrecommendations! Published in 2022 at "Computational Intelligence and Neuroscience"
DOI: 10.1155/2022/6241373
Abstract: The extractive summarization approach involves selecting the source document's salient sentences to build a summary. One of the most important aspects of extractive summarization is learning and modelling cross-sentence associations. Inspired by the popularity of… read more here.
Keywords: graph; attention graph; extractive summarization; model ... See more keywords