Overview of MSLR2022: A Shared Task on Multi-document Summarization for Literature Reviews

Lucy Lu Wang, Jay DeYoung, Byron Wallace


Abstract
We provide an overview of the MSLR2022 shared task on multi-document summarization for literature reviews. The shared task was hosted at the Third Scholarly Document Processing (SDP) Workshop at COLING 2022. For this task, we provided data consisting of gold summaries extracted from review papers along with the groups of input abstracts that were synthesized into these summaries, split into two subtasks. In total, six teams participated, making 10 public submissions, 6 to the Cochrane subtask and 4 to the MSˆ2 subtask. The top scoring systems reported over 2 points ROUGE-L improvement on the Cochrane subtask, though performance improvements are not consistently reported across all automated evaluation metrics; qualitative examination of the results also suggests the inadequacy of current evaluation metrics for capturing factuality and consistency on this task. Significant work is needed to improve system performance, and more importantly, to develop better methods for automatically evaluating performance on this task.
Anthology ID:
2022.sdp-1.20
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:
175–180
Language:
URL:
https://aclanthology.org/2022.sdp-1.20
DOI:
Bibkey:
Cite (ACL):
Lucy Lu Wang, Jay DeYoung, and Byron Wallace. 2022. Overview of MSLR2022: A Shared Task on Multi-document Summarization for Literature Reviews. In Proceedings of the Third Workshop on Scholarly Document Processing, pages 175–180, Gyeongju, Republic of Korea. Association for Computational Linguistics.
Cite (Informal):
Overview of MSLR2022: A Shared Task on Multi-document Summarization for Literature Reviews (Wang et al., sdp 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/paclic-22-ingestion/2022.sdp-1.20.pdf
Code
 allenai/mslr-shared-task
Data
Evidence Inference