We carry out experiments with deep learning models of summarization across the domains of news, personal stories, meetings, and medical articles in order to understand how content selection is performed. We find that many sophisticated features of state of the art extractive summarizers do not improve performance over simpler models. These results suggest that it is easier to create a summarizer for a new domain than previous work suggests and bring into question the benefit of deep learning models for summarization for those domains that do have massive datasets (i.e., news). At the same time, they suggest important questions for new research in summarization; namely, new forms of sentence representations or external knowledge sources are needed that are better suited to the summarization task.
翻译:我们在新闻、个人故事、会议和医学文章领域进行深层次总结学习模式的实验,以便了解内容选择是如何进行的;我们发现,与简单的模型相比,现代采掘总结器的许多尖端特征并不能改善业绩;这些结果表明,为新领域建立一个总结器比以前的工作更容易提出,并使人们质疑那些拥有大量数据集的领域(即新闻)的深度总结模型的好处;与此同时,它们提出了新的总结研究的重要问题;即需要新的句子表述形式或外部知识来源,这些形式更适合总结任务。