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

Existing fair ranking systems, especially those designed to be
demographically fair, assume that accurate demographic information about
individuals is available to the ranking algorithm. In practice, however, this
assumption may not hold -- in real-world contexts like ranking job applicants
or credit seekers, social and legal barriers may prevent algorithm operators
from collecting peoples' demographic information. In these cases, algorithm
operators may attempt to infer peoples' demographics and then supply these
inferences as inputs to the ranking algorithm.
In this study, we investigate how uncertainty and errors in demographic
inference impact the fairness offered by fair ranking algorithms. Using
simulations and three case studies with real datasets, we show how demographic
inferences drawn from real systems can lead to unfair rankings. Our results
suggest that developers should not use inferred demographic data as input to
fair ranking algorithms, unless the inferences are extremely accurate.

在真实世界的场景中，算法运营商为了在职位申请者或信贷申请者之间进行排名，可能通过推断人们的人口统计学信息并将其提供作为排名算法的输入，但本研究通过模拟和三个真实数据集的案例研究表明，实际系统中产生的人口推断可能会导致不公平的排名。因此，开发人员不应将推断出的人口统计数据作为公平排名算法的输入，除非推断结果非常准确。