natural-language-processing-5

Meeting Summarization

3260 papers • 126 benchmarks • 313 datasets

Generating a summary from meeting transcriptions. A survey for this task: Abstractive Meeting Summarization: A Survey

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Benchmarks

These leaderboards are used to track progress in meeting-summarization-5

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AMI Meeting Corpus

ICSI Meeting Corpus

Libraries

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Use these libraries to find meeting-summarization-5 models and implementations

Datasets

ELITR Minuting Corpus

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Most implemented papers

Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular Maximization

A. Tixier, M. Vazirgiannis, Guokan Shang, Wensi Ding, Zekun Zhang, Polykarpos Meladianos, J. Lorré•Sun May 13 2018

A novel graph-based framework for abstractive meeting speech summarization that is fully unsupervised and does not rely on any annotations to take exterior semantic knowledge into account, and to design custom diversity and informativeness measures.

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A Hierarchical Network for Abstractive Meeting Summarization with Cross-Domain Pretraining

Xuedong Huang, Michael Zeng, Chenguang Zhu, Ruochen Xu•Fri Apr 03 2020

A novel abstractive summary network that adapts to the meeting scenario is proposed with a hierarchical structure to accommodate long meeting transcripts and a role vector to depict the difference among speakers.

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Align then Summarize: Automatic Alignment Methods for Summarization Corpus Creation

Y. Estève, P. Tardy, D. Janiszek, Vincent Nguyen•Thu Apr 30 2020

It is suggested that considering a wider variety of tasks would lead to an improvement in the field, in terms of generalization and robustness, and it is shown that automatic alignment is relevant for data annotation since it leads to large improvement of almost +4 on all ROUGE scores on the summarization task.

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Summ^N: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents

Yusen Zhang, Dragomir R. Radev, A. Awadallah, Rui Zhang, Budhaditya Deb, Chen Henry Wu, Ansong Ni, Ziming Mao, Chenguang Zhu•Fri Oct 15 2021

Summ^N is the first multi-stage split-then-summarize framework for long input summarization and outperforms previous state-of-the-art methods by improving ROUGE scores on three long meeting summarization datasets AMI, ICSI, and QMSum.

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Abstractive Meeting Summarization: A Survey

M. Vazirgiannis, Virgile Rennard, Guokan Shang, Julie Hunter•Sun Aug 07 2022

An overview of the challenges raised by the task of abstractive meeting summarization and of the data sets, models, and evaluation metrics that have been used to tackle the problems is provided.

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How Domain Terminology Affects Meeting Summarization Performance

Fei Liu, Jia Jin Koay, A. Roustai, Xiaojin Dai, Dillon Burns, Alec Kerrigan•Sun Nov 01 2020

This paper creates gold-standard annotations for domain terminology on a sizable meeting corpus; they are known as jargon terms and reveal that domain terminology can have a substantial impact on summarization performance.

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Dialogue Discourse-Aware Graph Model and Data Augmentation for Meeting Summarization

Bing Qin, Xiaocheng Feng, Xiachong Feng, Xinwei Geng•Sun Dec 06 2020

A Dialogue Discourse-Dware Meeting Summarizer (DDAMS) to explicitly model the interaction between utterances in a meeting by modeling different discourse relations and a relational graph encoder, where the utterances and discourse relations are modeled in a graph interaction manner.

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

Asli Celikyilmaz, Tao Yu, Dragomir R. Radev, A. Zaidi, Xipeng Qiu, Da Yin, A. Awadallah, Yang Liu, Ming Zhong, Mutethia Mutuma, Rahul Jha•Mon Mar 22 2021

This work defines 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 introduces QMSum, a new benchmark for this task.

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Meeting Summarization with Pre-training and Clustering Methods

Wei Ji, Andras Huebner, Xiang Xiao•Mon Nov 15 2021

Improved performance of query-based meeting summarization is achieved by adding query embeddings to the input of the model, by using BART as an alternative language model, and by using clustering methods to extract key information at utterance level before feeding the text into summarization models.

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