Extractive Summarization of Telugu Text Using Modified Text Rank and Maximum Marginal Relevance
Article No.: 222, Pages 1 - 18
Abstract
With the rapid growth of digital content, there is a need for an automatic text summarizer to provide short text from a long text document. Many research works have been presented for extractive text summarization (ETS). This article mainly focuses on the graph-based ETS approach for multiple Telugu text documents. A modified Text-Rank algorithm is employed with the noun and verb count of each sentence in the text as the initial score of each node. To get the optimal features, a novel feature selection algorithm called improved Flamingo Search Algorithm is proposed in this article. Though graph-based ETS is an important approach, the generated summaries are redundant. To reduce the redundancy in the generated summary, maximum marginal relevance is combined with the modified Text-Rank. Different word-embedding techniques such as Fast-Text, Word2vec, TF-IDF, and one-hot encoding are utilized to experiment with the proposed approach. The performance of the proposed text summarization approach is evaluated with BLEU and ROUGE in terms of F-measure, precision, and recall.
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Index Terms
- Extractive Summarization of Telugu Text Using Modified Text Rank and Maximum Marginal Relevance
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September 2023
226 pages
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Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 22 September 2023
Online AM: 12 June 2023
Accepted: 23 April 2023
Revised: 13 July 2022
Received: 09 June 2022
Published in TALLIP Volume 22, Issue 9
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