Neural Query-Biased Abstractive Summarization Using Copying Mechanism

Ishigaki, Tatsuya; Huang, Hen-Hsen; Takamura, Hiroya; Chen, Hsin-Hsi; Okumura, Manabu

doi:10.1007/978-3-030-45442-5_22

“…Query-based summarization aims to generate a brief summary according to a source document and a given query. There are works studying this task (Daumé III and Marcu, 2006;Otterbacher et al, 2009;Wang et al, 2016;Litvak and Vanetik, 2017;Nema et al, 2017;Baumel et al, 2018;Ishigaki et al, 2020;Kulkarni et al, 2020;Laskar et al, 2020). However, the models focus on news (Dang, 2005(Dang, , 2006, debate (Nema et al, 2017), and Wikipedia (Zhu et al, 2019).…”

Section: Query-based Summarizationmentioning

confidence: 99%

QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization

Zhong¹,

Yin²,

Yu³

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Meetings are a key component of human collaboration. As increasing numbers of meetings are recorded and transcribed, meeting summaries have become essential to remind those who may or may not have attended the meetings about the key decisions made and the tasks to be completed. However, it is hard to create a single short summary that covers all the content of a long meeting involving multiple people and topics. In order to satisfy the needs of different types of users, we define a new query-based multi-domain meeting summarization task, where models have to select and summarize relevant spans of meetings in response to a query, and we introduce QMSum, a new benchmark for this task. QMSum consists of 1,808 query-summary pairs over 232 meetings in multiple domains. Besides, we investigate a locate-then-summarize method and evaluate a set of strong summarization baselines on the task. Experimental results and manual analysis reveal that QMSum presents significant challenges in long meeting summarization for future research. Dataset is available at https://github.com/Yale-LILY/ QMSum.

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“…They found utilizing transfer learning to be quite effective for the SD-QFAS task in that dataset. More recently, newer models based on the recurrent neural network architecture (Sutskever, Vinyals, and Le 2014) that did not utilize transfer learning failed to outperform the RSA model in terms of different ROUGE scores (Aryal and Chali 2020;Ishigaki et al 2020). This may indicate that the utilization of transfer learning to tackle the few-shot learning problem has a strong effect on performance improvement in the Debatepedia dataset.…”

Section: Single-document Query-focused Abstractive Text Summarizationmentioning

confidence: 99%

“…Here, 'R', 'P', and 'F' denote 'Recall', 'Precision', and 'F1', respectively, while 'QD' denotes 'Query-Document Attention' and 'BSA' denotes 'Bidirectional Self-Attention'. The results for the DDA, the Selection Driven, the Overlap-Wind, and the RSA model are collected from Nema et al (2017), Aryal and Chali (2020), Ishigaki et al (2020), andBaumel, Eyal, andElhadad (2018), respectively. on the MS-MARCO dataset.…”

Section: Tablementioning

confidence: 99%

Domain Adaptation with Pre-trained Transformers for Query-Focused Abstractive Text Summarization

Laskar

¹

,

Hoque

²

,

Huang

³

2022

Computational Linguistics

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The Query Focused Text Summarization (QFTS) task aims at building systems that generate the summary of the text document(s) based on the given query. A key challenge in addressing this task is the lack of large labeled data for training the summarization model. In this paper, we address this challenge by exploring a series of domain adaptation techniques. Given the recent success of pre-trained transformer models in a wide range of natural language processing tasks, we utilize such models to generate abstractive summaries for the QFTS task for both single-document and multi-document scenarios. For domain adaptation, we apply a variety of techniques using pre-trained transformer-based summarization models including transfer learning, weakly supervised learning, and distant supervision. Extensive experiments on six datasets show that our proposed approach is very effective in generating abstractive summaries for the QFTS task while setting a new state-of-the-art result in several datasets across a set of automatic and human evaluation metrics.

show abstract

“…Query-based summarization aims to generate a brief summary according to a source document and a given query. There are works studying this task (Daumé III and Marcu, 2006;Otterbacher et al, 2009;Wang et al, 2016;Litvak and Vanetik, 2017;Nema et al, 2017;Baumel et al, 2018;Ishigaki et al, 2020;Kulkarni et al, 2020;Laskar et al, 2020). However, the models focus on news (Dang, 2005(Dang, , 2006, debate (Nema et al, 2017), and Wikipedia (Zhu et al, 2019).…”

Section: Query-based Summarizationmentioning

confidence: 99%

QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization

Zhong¹,

Yin²,

Yu³

et al. 2021

Preprint

View full text Add to dashboard Cite

Meetings are a key component of human collaboration. As increasing numbers of meetings are recorded and transcribed, meeting summaries have become essential to remind those who may or may not have attended the meetings about the key decisions made and the tasks to be completed. However, it is hard to create a single short summary that covers all the content of a long meeting involving multiple people and topics. In order to satisfy the needs of different types of users, we define a new query-based multi-domain meeting summarization task, where models have to select and summarize relevant spans of meetings in response to a query, and we introduce QMSum, a new benchmark for this task. QMSum consists of 1,808 query-summary pairs over 232 meetings in multiple domains. Besides, we investigate a locate-then-summarize method and evaluate a set of strong summarization baselines on the task. Experimental results and manual analysis reveal that QMSum presents significant challenges in long meeting summarization for future research. Dataset is available at https://github.com/Yale-LILY/ QMSum.

show abstract

Neural Query-Biased Abstractive Summarization Using Copying Mechanism

Cited by 11 publications

References 13 publications

QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization

QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization

Domain Adaptation with Pre-trained Transformers for Query-Focused Abstractive Text Summarization

QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization

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