论文标题
新的章节抽象性摘要使用脊柱树意识到子句子内容选择
Novel Chapter Abstractive Summarization using Spinal Tree Aware Sub-Sentential Content Selection
论文作者
论文摘要
总结新章节是一项艰巨的任务，这是因为输入长度以及所需摘要中出现的句子从整章中的多个位置绘制内容的事实。我们提出了一种管道提取的提取方法，其中提取步骤过滤了传递给抽象成分的内容。非常漫长的输入也导致高度偏斜的数据集涉及进行抽取摘要的负面实例。因此，我们采取了提取的保证金排名损失，以鼓励在正面和负面实例之间分离。我们的提取成分在组成级别运行；我们对此问题的方法丰富了文本，其中脊柱树信息为提取模型提供了句法上下文（以成分的形式）。我们在现有的新颖章节数据集上的先前工作中报告的最佳结果表现出3.71 Rouge-1积分的提高。
Summarizing novel chapters is a difficult task due to the input length and the fact that sentences that appear in the desired summaries draw content from multiple places throughout the chapter. We present a pipelined extractive-abstractive approach where the extractive step filters the content that is passed to the abstractive component. Extremely lengthy input also results in a highly skewed dataset towards negative instances for extractive summarization; we thus adopt a margin ranking loss for extraction to encourage separation between positive and negative examples. Our extraction component operates at the constituent level; our approach to this problem enriches the text with spinal tree information which provides syntactic context (in the form of constituents) to the extraction model. We show an improvement of 3.71 Rouge-1 points over best results reported in prior work on an existing novel chapter dataset.
