Developed so far, multi-document summarization has reached its bottleneck due
to the lack of sufficient training data and diverse categories of documents.
Text classification just makes up for these deficiencies. In this paper, we
propose a novel summarization system called TCSum, which leverages plentiful
text classification data to improve the performance of multi-document
summarization. TCSum projects documents onto distributed representations which
act as a bridge between text classification and summarization. It also utilizes
the classification results to produce summaries of different styles. Extensive
experiments on DUC generic multi-document summarization datasets show that,
TCSum can achieve the state-of-the-art performance without using any
hand-crafted features and has the capability to catch the variations of summary
styles with respect to different text categories.

本文提出了一种叫做 TCSum 的新型摘要系统，它借助丰富的文本分类数据来改善多文档摘要的性能，通过将文档映射到分布式表示，利用分类结果来生成不同风格的摘要。实验结果表明，TCSum 在通用多文档摘要数据集上实现了最先进的性能，并具有在不同文本类别的情况下捕捉摘要样式变化的能力。