A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization
Jacob Parnell, Inigo Jauregi Unanue, Massimo Piccardi

TL;DR
This paper introduces a reinforcement learning approach using a multi-document coverage reward with RELAX to improve multi-document summarization, resulting in higher ROUGE and METEOR scores and better input document coverage.
Contribution
It proposes a novel reward function combining reference-based metrics and input coverage, fine-tuned with RELAX for efficient multi-document summarization.
Findings
Significant ROUGE score improvements (+0.95 pp)
Enhanced input document coverage across all documents
Competitive results with existing literature
Abstract
Multi-document summarization (MDS) has made significant progress in recent years, in part facilitated by the availability of new, dedicated datasets and capacious language models. However, a standing limitation of these models is that they are trained against limited references and with plain maximum-likelihood objectives. As for many other generative tasks, reinforcement learning (RL) offers the potential to improve the training of MDS models; yet, it requires a carefully-designed reward that can ensure appropriate leverage of both the reference summaries and the input documents. For this reason, in this paper we propose fine-tuning an MDS baseline with a reward that balances a reference-based metric such as ROUGE with coverage of the input documents. To implement the approach, we utilize RELAX (Grathwohl et al., 2018), a contemporary gradient estimator which is both low-variance and…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Text and Document Classification Technologies
