Multi-document Text Summarization using Semantic Word and Sentence Similarity: A Combined Approach

Rajendra Roul


Abstract
The exponential growth in the number of text documents produced daily on the web poses several difficulties to people who are responsible for collecting, organizing, and searching different textual content related to a particular topic. Automatic Text Summarization works well in this direction, which can review many documents and pull out the relevant information. But the limitations associated with automatic text summarization need to be removed by finding efficient workarounds. Although current research works have focused on this direction for further improvements, they still face many challenges. This paper proposes a combined semantic-based word and sentence similarity approach to summarize a corpus of text documents. To arrange the sentences in the final summary, KL-divergence technique is used. The experimental work is conducted using DUC datasets, and the obtained results are promising.
Anthology ID:
2021.icon-main.51
Volume:
Proceedings of the 18th International Conference on Natural Language Processing (ICON)
Month:
December
Year:
2021
Address:
National Institute of Technology Silchar, Silchar, India
Editors:
Sivaji Bandyopadhyay, Sobha Lalitha Devi, Pushpak Bhattacharyya
Venue:
ICON
SIG:
Publisher:
NLP Association of India (NLPAI)
Note:
Pages:
423–430
Language:
URL:
https://aclanthology.org/2021.icon-main.51
DOI:
Bibkey:
Cite (ACL):
Rajendra Roul. 2021. Multi-document Text Summarization using Semantic Word and Sentence Similarity: A Combined Approach. In Proceedings of the 18th International Conference on Natural Language Processing (ICON), pages 423–430, National Institute of Technology Silchar, Silchar, India. NLP Association of India (NLPAI).
Cite (Informal):
Multi-document Text Summarization using Semantic Word and Sentence Similarity: A Combined Approach (Roul, ICON 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.icon-main.51.pdf