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A New Method for Extractive Text Summarization Using Neural Networks by Sohini Roy Chowdhury; Kamal Sarkar is a Computer Science article available to read on EtoBox.

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Summarization aims at extracting the salient information from a document and presenting the extracted information in a condensed form. Most existing methods for extractive text summarization generate a summary from a document using a twostage process. In the first stage, the sentences are ranked based on their saliency scores and, in the second stage, the summary generation process starts with the top-ranked sentence and selects the next sentences one by one from the ranked list. To improve summary diversity, a sentence is included in the summary if the sentence is sufficiently dissimilar from the already selected sentences. Sentence selection is continued until the summary of the desired length is reached. The second stage is greedy in nature and it uses a predefined similarity threshold value to check the dissimilarity of a sentence with the already selected sentences. Due to this fixed similarity threshold which is manually tuned, in most cases, this approach fails to manage the diversity in a summary. This article proposes a summarization approach that uses a neural network-based learning model that learns to include a sentence in a summary by taking into account both the salie

Who reads A New Method for Extractive Text Summarization Using Neural Networks?

Author

Sohini Roy Chowdhury; Kamal Sarkar

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)