Abstract
The rapid proliferation of online content necessitates effective summarization methods, among which dynamic aspect-based summarization stands out. Unlike its traditional counterpart, which assumes a fixed set of known aspects, this approach adapts to the varied aspects of the input text. We introduce a novel multi-objective learning framework employing a Longformer-Encoder-Decoder for this task. The framework optimizes aspect number prediction, minimizes disparity between generated and reference summaries for each aspect, and maximizes dissimilarity across aspect-specific summaries. Extensive experiments show our method significantly outperforms baselines on three diverse datasets, largely due to the effective alignment of generated and reference aspect counts without sacrificing single-aspect summarization quality.- Anthology ID:
- 2024.findings-acl.165
- Volume:
- Findings of the Association for Computational Linguistics ACL 2024
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand and virtual meeting
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2814–2827
- Language:
- URL:
- https://aclanthology.org/2024.findings-acl.165
- DOI:
- 10.18653/v1/2024.findings-acl.165
- Cite (ACL):
- Xiaobo Guo and Soroush Vosoughi. 2024. MODABS: Multi-Objective Learning for Dynamic Aspect-Based Summarization. In Findings of the Association for Computational Linguistics ACL 2024, pages 2814–2827, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.
- Cite (Informal):
- MODABS: Multi-Objective Learning for Dynamic Aspect-Based Summarization (Guo & Vosoughi, Findings 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2024.findings-acl.165.pdf