IITP-AI-NLP-ML@ CL-SciSumm 2020, CL-LaySumm 2020, LongSumm 2020

Santosh Kumar Mishra, Harshavardhan Kundarapu, Naveen Saini, Sriparna Saha, Pushpak Bhattacharyya


Abstract
The publication rate of scientific literature increases rapidly, which poses a challenge for researchers to keep themselves updated with new state-of-the-art. Scientific document summarization solves this problem by summarizing the essential fact and findings of the document. In the current paper, we present the participation of IITP-AI-NLP-ML team in three shared tasks, namely, CL-SciSumm 2020, LaySumm 2020, LongSumm 2020, which aims to generate medium, lay, and long summaries of the scientific articles, respectively. To solve CL-SciSumm 2020 and LongSumm 2020 tasks, three well-known clustering techniques are used, and then various sentence scoring functions, including textual entailment, are used to extract the sentences from each cluster for a summary generation. For LaySumm 2020, an encoder-decoder based deep learning model has been utilized. Performances of our developed systems are evaluated in terms of ROUGE measures on the associated datasets with the shared task.
Anthology ID:
2020.sdp-1.30
Volume:
Proceedings of the First Workshop on Scholarly Document Processing
Month:
November
Year:
2020
Address:
Online
Editors:
Muthu Kumar Chandrasekaran, Anita de Waard, Guy Feigenblat, Dayne Freitag, Tirthankar Ghosal, Eduard Hovy, Petr Knoth, David Konopnicki, Philipp Mayr, Robert M. Patton, Michal Shmueli-Scheuer
Venue:
sdp
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
270–276
Language:
URL:
https://aclanthology.org/2020.sdp-1.30
DOI:
10.18653/v1/2020.sdp-1.30
Bibkey:
Cite (ACL):
Santosh Kumar Mishra, Harshavardhan Kundarapu, Naveen Saini, Sriparna Saha, and Pushpak Bhattacharyya. 2020. IITP-AI-NLP-ML@ CL-SciSumm 2020, CL-LaySumm 2020, LongSumm 2020. In Proceedings of the First Workshop on Scholarly Document Processing, pages 270–276, Online. Association for Computational Linguistics.
Cite (Informal):
IITP-AI-NLP-ML@ CL-SciSumm 2020, CL-LaySumm 2020, LongSumm 2020 (Mishra et al., sdp 2020)
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PDF:
https://aclanthology.org/2020.sdp-1.30.pdf