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        <title>Instructive Dialogue Summarization with Query Aggregations</title>
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        <namePart type="given">Zhengyuan</namePart>
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        <dateIssued>2023-12</dateIssued>
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            <title>Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing</title>
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    <abstract>Conventional dialogue summarization methods directly generate summaries and do not consider user’s specific interests. This poses challenges in cases where the users are more focused on particular topics or aspects. With the advancement of instruction-finetuned language models, we introduce instruction-tuning to dialogues to expand the capability set of dialogue summarization models. To overcome the scarcity of instructive dialogue summarization data, we propose a three-step approach to synthesize high-quality query-based summarization triples. This process involves summary-anchored query generation, query filtering and query-based summary generation. By training a unified model called InstructDS (Instructive Dialogue Summarization) on three summarization datasets with multi-purpose instructive triples, we expand the capability of dialogue summarization models. We evaluate our method on four datasets, including dialogue summarization and dialogue reading comprehension. Experimental results show that our approach outperforms the state-of-the-art models and even models with larger sizes. Additionally, our model exhibits higher generalizability and faithfulness, as confirmed by human subjective evaluations.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.474</identifier>
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%0 Conference Proceedings
%T Instructive Dialogue Summarization with Query Aggregations
%A Wang, Bin
%A Liu, Zhengyuan
%A Chen, Nancy
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F wang-etal-2023-instructive
%X Conventional dialogue summarization methods directly generate summaries and do not consider user’s specific interests. This poses challenges in cases where the users are more focused on particular topics or aspects. With the advancement of instruction-finetuned language models, we introduce instruction-tuning to dialogues to expand the capability set of dialogue summarization models. To overcome the scarcity of instructive dialogue summarization data, we propose a three-step approach to synthesize high-quality query-based summarization triples. This process involves summary-anchored query generation, query filtering and query-based summary generation. By training a unified model called InstructDS (Instructive Dialogue Summarization) on three summarization datasets with multi-purpose instructive triples, we expand the capability of dialogue summarization models. We evaluate our method on four datasets, including dialogue summarization and dialogue reading comprehension. Experimental results show that our approach outperforms the state-of-the-art models and even models with larger sizes. Additionally, our model exhibits higher generalizability and faithfulness, as confirmed by human subjective evaluations.
%R 10.18653/v1/2023.emnlp-main.474
%U https://aclanthology.org/2023.emnlp-main.474
%U https://doi.org/10.18653/v1/2023.emnlp-main.474
%P 7630-7653

Markdown (Informal)

[Instructive Dialogue Summarization with Query Aggregations](https://aclanthology.org/2023.emnlp-main.474) (Wang et al., EMNLP 2023)

• Instructive Dialogue Summarization with Query Aggregations (Wang et al., EMNLP 2023)

ACL

• Bin Wang, Zhengyuan Liu, and Nancy Chen. 2023. Instructive Dialogue Summarization with Query Aggregations. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 7630–7653, Singapore. Association for Computational Linguistics.