Generating Summaries Through Unigram and Bigram: Text Summarization

Nesreen Mohammad Alsharman and Inna V. Pivkina
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Nesreen Mohammad Alsharman: WISE, Amman, Jordan
Inna V. Pivkina: NMSU, USA

International Journal of Information Technology and Web Engineering (IJITWE), 2020, vol. 15, issue 1, 64-74

Abstract: This article describes a new method for generating extractive summaries directly via unigram and bigram extraction techniques. The methodology uses the selective part of speech tagging to extract significant unigrams and bigrams from a set of sentences. Extracted unigrams and bigrams along with other features are used to build a final summary. A new selective rule-based part of speech tagging system is developed that concentrates on the most important parts of speech for summarizations: noun, verb, and adjective. Other parts of speech such as prepositions, articles, adverbs, etc., play a lesser role in determining the meaning of sentences; therefore, they are not considered when choosing significant unigrams and bigrams. The proposed method is tested on two problem domains: citations and opinosis data sets. Results show that the proposed method performs better than Text-Rank, LexRank, and Edmundson summarization methods. The proposed method is general enough to summarize texts from any domain.

Date: 2020
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