Abstract
Long document summarisation, a challenging summarisation scenario, is the focus of the recently proposed LongSumm shared task. One of the limitations of this shared task has been its use of a single family of metrics for evaluation (the ROUGE metrics). In contrast, other fields, like text generation, employ multiple metrics. We replicated the LongSumm evaluation using multiple test set samples (vs. the single test set of the official shared task) and investigated how different metrics might complement each other in this evaluation framework. We show that under this more rigorous evaluation, (1) some of the key learnings from Longsumm 2020 and 2021 still hold, but the relative ranking of systems changes, and (2) the use of additional metrics reveals additional high-quality summaries missed by ROUGE, and (3) we show that SPICE is a candidate metric for summarisation evaluation for LongSumm.- Anthology ID:
- 2022.sdp-1.13
- 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:
- 115–125
- Language:
- URL:
- https://aclanthology.org/2022.sdp-1.13
- DOI:
- Cite (ACL):
- Cai Yang and Stephen Wan. 2022. Investigating Metric Diversity for Evaluating Long Document Summarisation. In Proceedings of the Third Workshop on Scholarly Document Processing, pages 115–125, Gyeongju, Republic of Korea. Association for Computational Linguistics.
- Cite (Informal):
- Investigating Metric Diversity for Evaluating Long Document Summarisation (Yang & Wan, sdp 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.sdp-1.13.pdf
- Code
- caiyangcy/sdp-longsumm-metric-diversity