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arXiv · 2502.04535

A Decoding Algorithm for Length-Control Summarization Based on Directed Acyclic Transformers

Chenyang Huang·Hao Zhou·Cameron Jen·Kangjie Zheng·Osmar R. Zaïane·Lili Mou
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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 \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.

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Chenyang Huang, Hao Zhou, Cameron Jen, Kangjie Zheng, Osmar R. Zaïane, Lili Mou. 2025-02-06. A Decoding Algorithm for Length-Control Summarization Based on Directed Acyclic Transformers. https://arxiv.org/abs/2502.04535

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