论文标题
无监督的抽象对话摘要用单词图和POV转换
Unsupervised Abstractive Dialogue Summarization with Word Graphs and POV Conversion
论文作者
论文摘要
我们通过利用多句子压缩图来推进无监督的抽象对话摘要中最新的。从关于单词图的良好假设开始，我们提出简单但可靠的路径秩和主题分割方案。我们的方法的鲁棒性在跨多个领域的数据集中证明，包括会议，访谈，电影脚本和日常对话。我们还确定了可能通过深度学习来增强基于启发式系统的途径。我们开源的代码，为将来的无监督对话摘要提供了强大的，可再现的基准。
We advance the state-of-the-art in unsupervised abstractive dialogue summarization by utilizing multi-sentence compression graphs. Starting from well-founded assumptions about word graphs, we present simple but reliable path-reranking and topic segmentation schemes. Robustness of our method is demonstrated on datasets across multiple domains, including meetings, interviews, movie scripts, and day-to-day conversations. We also identify possible avenues to augment our heuristic-based system with deep learning. We open-source our code, to provide a strong, reproducible baseline for future research into unsupervised dialogue summarization.
