Text summarization is one of the most challenging and interesting problems in
NLP. Although much attention has been paid to summarizing structured text like
news reports or encyclopedia articles, summarizing conversations---an essential
part of human-human/machine interaction where most important pieces of
information are scattered across various utterances of different
speakers---remains relatively under-investigated. This work proposes a
multi-view sequence-to-sequence model by first extracting conversational
structures of unstructured daily chats from different views to represent
conversations and then utilizing a multi-view decoder to incorporate different
views to generate dialogue summaries. Experiments on a large-scale dialogue
summarization corpus demonstrated that our methods significantly outperformed
previous state-of-the-art models via both automatic evaluations and human
judgment. We also discussed specific challenges that current approaches faced
with this task. We have publicly released our code at
this https URL

本研究提出了一种多视角序列到序列模型，其通过提取来自不同视角的非结构化日常聊天中的会话结构来表示会话，然后利用多视角解码器来合并不同视角生成对话摘要，在大规模对话摘要语料库上的实验结果表明，本研究方法在自动评估和人类判断方面均明显优于之前的最新模型。