CNLP-NITS @ LongSumm 2021: TextRank Variant for Generating Long Summaries

Darsh Kaushik, Abdullah Faiz Ur Rahman Khilji, Utkarsh Sinha, Partha Pakray


Abstract
The huge influx of published papers in the field of machine learning makes the task of summarization of scholarly documents vital, not just to eliminate the redundancy but also to provide a complete and satisfying crux of the content. We participated in LongSumm 2021: The 2nd Shared Task on Generating Long Summaries for scientific documents, where the task is to generate long summaries for scientific papers provided by the organizers. This paper discusses our extractive summarization approach to solve the task. We used TextRank algorithm with the BM25 score as a similarity function. Even after being a graph-based ranking algorithm that does not require any learning, TextRank produced pretty decent results with minimal compute power and time. We attained 3rd rank according to ROUGE-1 scores (0.5131 for F-measure and 0.5271 for recall) and performed decently as shown by the ROUGE-2 scores.
Anthology ID:
2021.sdp-1.13
Volume:
Proceedings of the Second Workshop on Scholarly Document Processing
Month:
June
Year:
2021
Address:
Online
Editors:
Iz Beltagy, Arman Cohan, Guy Feigenblat, Dayne Freitag, Tirthankar Ghosal, Keith Hall, Drahomira Herrmannova, Petr Knoth, Kyle Lo, Philipp Mayr, Robert M. Patton, Michal Shmueli-Scheuer, Anita de Waard, Kuansan Wang, Lucy Lu Wang
Venue:
sdp
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
103–109
Language:
URL:
https://aclanthology.org/2021.sdp-1.13
DOI:
10.18653/v1/2021.sdp-1.13
Bibkey:
Cite (ACL):
Darsh Kaushik, Abdullah Faiz Ur Rahman Khilji, Utkarsh Sinha, and Partha Pakray. 2021. CNLP-NITS @ LongSumm 2021: TextRank Variant for Generating Long Summaries. In Proceedings of the Second Workshop on Scholarly Document Processing, pages 103–109, Online. Association for Computational Linguistics.
Cite (Informal):
CNLP-NITS @ LongSumm 2021: TextRank Variant for Generating Long Summaries (Kaushik et al., sdp 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.sdp-1.13.pdf