GUIR @ MuP 2022: Towards Generating Topic-aware Multi-perspective Summaries for Scientific Documents

Sajad Sotudeh, Nazli Goharian


Abstract
This paper presents our approach for the MuP 2022 shared task —-Multi-Perspective Scientific Document Summarization, where the objective is to enable summarization models to explore methods for generating multi-perspective summaries for scientific papers. We explore two orthogonal ways to cope with this task. The first approach involves incorporating a neural topic model (i.e., NTM) into the state-of-the-art abstractive summarizer (LED); the second approach involves adding a two-step summarizer that extracts the salient sentences from the document and then writes abstractive summaries from those sentences. Our latter model outperformed our other submissions on the official test set. Specifically, among 10 participants (including organizers’ baseline) who made their results public with 163 total runs. Our best system ranks first in Rouge-1 (F), and second in Rouge-1 (R), Rouge-2 (F) and Average Rouge (F) scores.
Anthology ID:
2022.sdp-1.34
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:
273–278
Language:
URL:
https://aclanthology.org/2022.sdp-1.34
DOI:
Bibkey:
Cite (ACL):
Sajad Sotudeh and Nazli Goharian. 2022. GUIR @ MuP 2022: Towards Generating Topic-aware Multi-perspective Summaries for Scientific Documents. In Proceedings of the Third Workshop on Scholarly Document Processing, pages 273–278, Gyeongju, Republic of Korea. Association for Computational Linguistics.
Cite (Informal):
GUIR @ MuP 2022: Towards Generating Topic-aware Multi-perspective Summaries for Scientific Documents (Sotudeh & Goharian, sdp 2022)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.sdp-1.34.pdf