We present MeeQA, a dataset for natural-language question answering over
meeting transcripts. It includes real questions asked during meetings by its
participants. The dataset contains 48K question-answer pairs, extracted from
422 meeting transcripts, spanning multiple domains. Questions in transcripts
pose a special challenge as they are not always clear, and considerable context
may be required in order to provide an answer. Further, many questions asked
during meetings are left unanswered. To improve baseline model performance on
this type of questions, we also propose a novel loss function, \emph{Flat
Hierarchical Loss}, designed to enhance performance over questions with no
answer in the text. Our experiments demonstrate the advantage of using our
approach over standard QA models.

本研究提出了 MeeQA 数据集，包括来自会议记录中实际提出的 48K 个问题 - 答案对，并提出了一种用于提高基线模型性能的新型损失函数 Flat Hierarchical Loss，实验证明该方法优于标准 QA 模型。

MeeQA：会议记录中的自然问题

MeeQA: Natural Questions in Meeting Transcripts

ICASSP2023 General Meeting Understanding and Generation Challenge (MUG)
focuses on prompting a wide range of spoken language processing (SLP) research
on meeting transcripts, as SLP applications are critical to improve users'
efficiency in grasping important information in meetings. MUG includes five
tracks, including topic segmentation, topic-level and session-level extractive
summarization, topic title generation, keyphrase extraction, and action item
detection. To facilitate MUG, we construct and release a large-scale meeting
dataset, the AliMeeting4MUG Corpus.

ICASSP2023 General Meeting Understanding and Generation Challenge (MUG) aims to improve users' efficiency in meetings by prompting research on spoken language processing, with five tracks including topic segmentation, summarization, title generation, keyphrase extraction, and action item detection. A large-scale meeting dataset, the AliMeeting4MUG Corpus, has been constructed and released to facilitate research.

ICASSP 2023 年总会理解与生成挑战赛 (MUG) 概述

Overview of the ICASSP 2023 General Meeting Understanding and Generation Challenge (MUG)

Recently abstractive spoken language summarization raises emerging research
interest, and neural sequence-to-sequence approaches have brought significant
performance improvement. However, summarizing long meeting transcripts remains
challenging. Due to the large length of source contents and targeted summaries,
neural models are prone to be distracted on the context, and produce summaries
with degraded quality. Moreover, pre-trained language models with input length
limitations cannot be readily applied to long sequences. In this work, we first
analyze the linguistic characteristics of meeting transcripts on a
representative corpus, and find that the sentences comprising the summary
correlate with the meeting agenda. Based on this observation, we propose a
dynamic sliding window strategy for meeting summarization. Experimental results
show that performance benefit from the proposed method, and outputs obtain
higher factual consistency than the base model.

本研究发现会议摘要的句子与会议议程有关，基于此提出了一种动态滑动窗口策略来解决长会议记录的自动文本摘要问题。实验结果表明，相较于传统模型，该方法获得了更高的文本一致性。