@inproceedings{huang-etal-2024-decoding,
title = "A Decoding Algorithm for Length-Control Summarization Based on Directed Acyclic Transformers",
author = "Huang, Chenyang and
Zhou, Hao and
Jen, Cameron and
Zheng, Kangjie and
Zaiane, Osmar and
Mou, Lili",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.677/",
doi = "10.18653/v1/2024.findings-emnlp.677",
pages = "11572--11583",
abstract = "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 \textit{path} to connect them. In addition, we propose a Sequence Maximum a Posteriori (Seq-MAP) 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 dataset demonstrate our state-of-the-art performance for length-control summarization."
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<abstract>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 path to connect them. In addition, we propose a Sequence Maximum a Posteriori (Seq-MAP) 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 dataset demonstrate our state-of-the-art performance for length-control summarization.</abstract>
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%0 Conference Proceedings
%T A Decoding Algorithm for Length-Control Summarization Based on Directed Acyclic Transformers
%A Huang, Chenyang
%A Zhou, Hao
%A Jen, Cameron
%A Zheng, Kangjie
%A Zaiane, Osmar
%A Mou, Lili
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F huang-etal-2024-decoding
%X 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 path to connect them. In addition, we propose a Sequence Maximum a Posteriori (Seq-MAP) 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 dataset demonstrate our state-of-the-art performance for length-control summarization.
%R 10.18653/v1/2024.findings-emnlp.677
%U https://aclanthology.org/2024.findings-emnlp.677/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.677
%P 11572-11583
Markdown (Informal)
[A Decoding Algorithm for Length-Control Summarization Based on Directed Acyclic Transformers](https://aclanthology.org/2024.findings-emnlp.677/) (Huang et al., Findings 2024)
ACL