Entailment and spectral clustering based single and multiple document summarization
Автор: Anand Gupta, Manpreet Kaur, Ahsaas Bajaj, Ansh Khanna
Журнал: International Journal of Intelligent Systems and Applications @ijisa
Статья в выпуске: 4 vol.11, 2019 года.
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Text connectedness is an important feature for content selection in text summarization methods. Recently, Textual Entailment (TE) has been successfully employed to measure sentence connectedness in order to determine sentence salience in single document text summarization. In literature, Analog Textual Entailment and Spectral Clustering (ATESC) is one such method which has used TE to compute inter-sentence connectedness scores. These scores are used to compute salience of sentences and are further utilized by Spectral Clustering algorithm to create segments of sentences. Finally, the most salient sentences are extracted from the most salient segments for inclusion in the final summary. The method has shown good performance earlier. But the authors observe that TE has never been employed for the task of multi-document summarization. Therefore, this paper has proposed ATESC based new methods for the same task. The experiments conducted on DUC 2003 and 2004 datasets reveal that the notion of Textual Entailment along with Spectral Clustering algorithm proves to be an effective duo for redundancy removal and generating informative summaries in multi-document summarization. Moreover, the proposed methods have exhibited faster execution times.
Automatic Summarization, Textual Entailment, Spectral Clustering, Information Retrieval, Extractive Techniques, Natural Language Processing
Короткий адрес: https://sciup.org/15016586
IDR: 15016586 | DOI: 10.5815/ijisa.2019.04.04
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