Manpreet Kaur

Work place: Netaji Subhas Institute of Technology, New Delhi, India



Research Interests: Computer systems and computational processes, Natural Language Processing, Systems Architecture, Analysis of Algorithms


Manpreet Kaur is a Ph.D scholar at Netaji Subhas Institute of   She is working at Manav Rachna University, Faridabad, Haryana. She is doing her research work in Text summarization. Her other research interests include Natural Language Processing, Textual Entailment, Sentiment Analysis, Question Answering System etc.

Author Articles
Entailment and Spectral Clustering based Single and Multiple Document Summarization

By Anand Gupta Manpreet Kaur Ahsaas Bajaj Ansh Khanna

DOI:, Pub. Date: 8 Apr. 2019

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.

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