Team AINLPML @ MuP in SDP 2021: Scientific Document Summarization by End-to-End Extractive and Abstractive Approach

Sandeep Kumar, Guneet Singh Kohli, Kartik Shinde, Asif Ekbal

Abstract
This paper introduces the proposed summarization system of the AINLPML team for the First Shared Task on Multi-Perspective Scientific Document Summarization at SDP 2022. We present a method to produce abstractive summaries of scientific documents. First, we perform an extractive summarization step to identify the essential part of the paper. The extraction step includes utilizing a contributing sentence identification model to determine the contributing sentences in selected sections and portions of the text. In the next step, the extracted relevant information is used to condition the transformer language model to generate an abstractive summary. In particular, we fine-tuned the pre-trained BART model on the extracted summary from the previous step. Our proposed model successfully outperformed the baseline provided by the organizers by a significant margin. Our approach achieves the best average Rouge F1 Score, Rouge-2 F1 Score, and Rouge-L F1 Score among all submissions.
Anthology ID:
2022.sdp-1.36
Volume:
Proceedings of the Third Workshop on Scholarly Document Processing
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
sdp
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
285–290
Language:
URL:
https://aclanthology.org/2022.sdp-1.36
DOI:
Bibkey:
Cite (ACL):
Sandeep Kumar, Guneet Singh Kohli, Kartik Shinde, and Asif Ekbal. 2022. Team AINLPML @ MuP in SDP 2021: Scientific Document Summarization by End-to-End Extractive and Abstractive Approach. In Proceedings of the Third Workshop on Scholarly Document Processing, pages 285–290, Gyeongju, Republic of Korea. Association for Computational Linguistics.
Cite (Informal):
Team AINLPML @ MuP in SDP 2021: Scientific Document Summarization by End-to-End Extractive and Abstractive Approach (Kumar et al., sdp 2022)
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