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

• URL: http://arxiv.org/abs/2502.04535v1
• Date: Thu, 06 Feb 2025 22:12:55 GMT
• Title: A Decoding Algorithm for Length-Control Summarization Based on Directed Acyclic Transformers
• Authors: Chenyang Huang, Hao Zhou, Cameron Jen, Kangjie Zheng, Osmar R. Zaïane, Lili Mou,
• Abstract summary: 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. Our approach allows for multiple plausible sequence fragments and predicts a emphpath to connect them.
• Score: 32.53051395472311
• License:
• 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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