论文标题
多视图序列到序列模型，具有对话结构，用于抽象对话摘要
Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization
论文作者
论文摘要
文本摘要是NLP中最具挑战性和最有趣的问题之一。尽管已经引起了很多关注，以总结结构化的文本，例如新闻报道或百科全书文章，但总结了对话 - 这是人类/机器互动的重要组成部分，其中大多数重要的信息散布在不同扬声器的各种话语中 - 仍然相对不足。这项工作提出了一个多视图序列到序列模型，首先从不同视图中提取非结构化日常聊天的对话结构来表示对话，然后利用多视图解码器来包含不同的视图来生成对话摘要。大规模对话摘要的实验表明，通过自动评估和人类判断，我们的方法显着超过了先前的最新模型。我们还讨论了当前方法在此任务中面临的具体挑战。我们已在https://github.com/gt-salt/multi-view-seq2seq上公开发布了代码。
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 https://github.com/GT-SALT/Multi-View-Seq2Seq.
