Abstract
The MuP-2022 shared task focuses on multiperspective scientific document summarization. Given a scientific document, with multiple reference summaries, our goal was to develop a model that can produce a generic summary covering as many aspects of the document as covered by all of its reference summaries. This paper describes our best official model, a finetuned BART-large, along with a discussion on the challenges of this task and some of our unofficial models including SOTA generation models. Our submitted model out performedthe given, MuP 2022 shared task, baselines on ROUGE-2, ROUGE-L and average ROUGE F1-scores. Code of our submission can be ac- cessed here.- Anthology ID:
- 2022.sdp-1.35
- 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:
- 279–284
- Language:
- URL:
- https://aclanthology.org/2022.sdp-1.35
- DOI:
- Cite (ACL):
- Ashok Urlana, Nirmal Surange, and Manish Shrivastava. 2022. LTRC @MuP 2022: Multi-Perspective Scientific Document Summarization Using Pre-trained Generation Models. In Proceedings of the Third Workshop on Scholarly Document Processing, pages 279–284, Gyeongju, Republic of Korea. Association for Computational Linguistics.
- Cite (Informal):
- LTRC @MuP 2022: Multi-Perspective Scientific Document Summarization Using Pre-trained Generation Models (Urlana et al., sdp 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.sdp-1.35.pdf
- Data
- SciTLDR