A Decoding Algorithm for Length-Control Summarization Based on Directed Acyclic Transformers
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866913682879938560 |
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| author | Huang, Chenyang Zhou, Hao Jen, Cameron Zheng, Kangjie Zaïane, Osmar R. Mou, Lili |
| author_facet | Huang, Chenyang Zhou, Hao Jen, Cameron Zheng, Kangjie Zaïane, Osmar R. Mou, Lili |
| contents | Length-control summarization aims to condense long texts into a short one within a certain length limit. Previous approaches often use autoregressive (AR) models and treat the length requirement as a soft constraint, which may not always be satisfied. In this study, we propose a novel length-control decoding algorithm based on the Directed Acyclic Transformer (DAT). Our approach allows for multiple plausible sequence fragments and predicts a \emph{path} to connect them. In addition, we propose a Sequence Maximum a Posteriori (SeqMAP) decoding algorithm that marginalizes different possible paths and finds the most probable summary satisfying the length budget. Our algorithm is based on beam search, which further facilitates a reranker for performance improvement. Experimental results on the Gigaword and DUC2004 datasets demonstrate our state-of-the-art performance for length-control summarization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_04535 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Decoding Algorithm for Length-Control Summarization Based on Directed Acyclic Transformers Huang, Chenyang Zhou, Hao Jen, Cameron Zheng, Kangjie Zaïane, Osmar R. Mou, Lili Computation and Language Length-control summarization aims to condense long texts into a short one within a certain length limit. Previous approaches often use autoregressive (AR) models and treat the length requirement as a soft constraint, which may not always be satisfied. In this study, we propose a novel length-control decoding algorithm based on the Directed Acyclic Transformer (DAT). Our approach allows for multiple plausible sequence fragments and predicts a \emph{path} to connect them. In addition, we propose a Sequence Maximum a Posteriori (SeqMAP) decoding algorithm that marginalizes different possible paths and finds the most probable summary satisfying the length budget. Our algorithm is based on beam search, which further facilitates a reranker for performance improvement. Experimental results on the Gigaword and DUC2004 datasets demonstrate our state-of-the-art performance for length-control summarization. |
| title | A Decoding Algorithm for Length-Control Summarization Based on Directed Acyclic Transformers |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2502.04535 |