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        <title>Towards Zero-Shot Conditional Summarization with Adaptive Multi-Task Fine-Tuning</title>
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    <name type="personal">
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    <abstract>Automatic summarization research has traditionally focused on providing high quality general-purpose summaries of documents. However, there are many applications which require more specific summaries, such as supporting question answering or topic-based literature discovery. In this paper we study the problem of conditional summarization in which content selection and surface realization are explicitly conditioned on an ad-hoc natural language question or topic description. Because of the difficulty in obtaining sufficient reference summaries to support arbitrary conditional summarization, we explore the use of multi-task fine-tuning (MTFT) on twenty-one natural language tasks to enable zero-shot conditional summarization on five tasks. We present four new summarization datasets, two novel “online” or adaptive task-mixing strategies, and report zero-shot performance using T5 and BART, demonstrating that MTFT can improve zero-shot summarization quality.</abstract>
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%0 Conference Proceedings
%T Towards Zero-Shot Conditional Summarization with Adaptive Multi-Task Fine-Tuning
%A Goodwin, Travis
%A Savery, Max
%A Demner-Fushman, Dina
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F goodwin-etal-2020-towards
%X Automatic summarization research has traditionally focused on providing high quality general-purpose summaries of documents. However, there are many applications which require more specific summaries, such as supporting question answering or topic-based literature discovery. In this paper we study the problem of conditional summarization in which content selection and surface realization are explicitly conditioned on an ad-hoc natural language question or topic description. Because of the difficulty in obtaining sufficient reference summaries to support arbitrary conditional summarization, we explore the use of multi-task fine-tuning (MTFT) on twenty-one natural language tasks to enable zero-shot conditional summarization on five tasks. We present four new summarization datasets, two novel “online” or adaptive task-mixing strategies, and report zero-shot performance using T5 and BART, demonstrating that MTFT can improve zero-shot summarization quality.
%R 10.18653/v1/2020.findings-emnlp.289
%U https://aclanthology.org/2020.findings-emnlp.289/
%U https://doi.org/10.18653/v1/2020.findings-emnlp.289
%P 3215-3226

Markdown (Informal)

[Towards Zero-Shot Conditional Summarization with Adaptive Multi-Task Fine-Tuning](https://aclanthology.org/2020.findings-emnlp.289/) (Goodwin et al., Findings 2020)

• Towards Zero-Shot Conditional Summarization with Adaptive Multi-Task Fine-Tuning (Goodwin et al., Findings 2020)

ACL

• Travis Goodwin, Max Savery, and Dina Demner-Fushman. 2020. Towards Zero-Shot Conditional Summarization with Adaptive Multi-Task Fine-Tuning. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3215–3226, Online. Association for Computational Linguistics.