Recent advances in deep learning, and especially the invention of
encoder-decoder architectures, has significantly improved the performance of
abstractive summarization systems. While the majority of research has focused
on written documents, we have observed an increasing interest in the
summarization of dialogues and multi-party conversation over the past few
years. A system that could reliably transform the audio or transcript of a
human conversation into an abridged version that homes in on the most important
points of the discussion would be valuable in a wide variety of real-world
contexts, from business meetings to medical consultations to customer service
calls. This paper focuses on abstractive summarization for multi-party
meetings, providing a survey of the challenges, datasets and systems relevant
to this task and a discussion of promising directions for future study.

本文对多方会议情境下的自动摘要这一任务涉及到的难点、数据集以及系统进行了综述，并介绍了未来的研究方向。

抽象会议总结：一项调查

Abstractive Meeting Summarization: A Survey

Recently, various neural models for multi-party conversation (MPC) have
achieved impressive improvements on a variety of tasks such as addressee
recognition, speaker identification and response prediction. However, these
existing methods on MPC usually represent interlocutors and utterances
individually and ignore the inherent complicated structure in MPC which may
provide crucial interlocutor and utterance semantics and would enhance the
conversation understanding process. To this end, we present MPC-BERT, a
pre-trained model for MPC understanding that considers learning who says what
to whom in a unified model with several elaborated self-supervised tasks.
Particularly, these tasks can be generally categorized into (1) interlocutor
structure modeling including reply-to utterance recognition, identical speaker
searching and pointer consistency distinction, and (2) utterance semantics
modeling including masked shared utterance restoration and shared node
detection. We evaluate MPC-BERT on three downstream tasks including addressee
recognition, speaker identification and response selection. Experimental
results show that MPC-BERT outperforms previous methods by large margins and
achieves new state-of-the-art performance on all three downstream tasks at two
benchmarks.

MPC-BERT 是一种预训练的神经模型，通过学习谁对谁说什么，结合几个自监督任务，在多方对话任务中表现出比以前的方法更好的性能。