In this work, we aim at developing an extractive summarizer in the
multi-document setting. We implement a rank based sentence selection using
continuous vector representations along with key-phrases. Furthermore, we
propose a model to tackle summary coherence for increasing readability. We
conduct experiments on the Document Understanding Conference (DUC) 2004
datasets using ROUGE toolkit. Our experiments demonstrate that the methods
bring significant improvements over the state of the art methods in terms of
informativity and coherence.

该研究旨在开发一种在多文档环境中的提取式摘要生成器，使用基于排名的句子选择方法以及连续向量表示和关键短语，并提出了一种模型来解决摘要连贯性问题，以提高可读性。实验结果表明，在信息量和连贯性方面，该方法相对于现有方法有了显著的改进。

面向连贯多文档摘要的提取排序算法

Extract with Order for Coherent Multi-Document Summarization

Most existing models for multilingual natural language processing (NLP) treat
language as a discrete category, and make predictions for either one language
or the other. In contrast, we propose using continuous vector representations
of language. We show that these can be learned efficiently with a
character-based neural language model, and used to improve inference about
language varieties not seen during training. In experiments with 1303 Bible
translations into 990 different languages, we empirically explore the capacity
of multilingual language models, and also show that the language vectors
capture genetic relationships between languages.

本研究提出使用连续向量表示语言的方法，可以用于多语言自然语言处理，实验结果表明这种方法可以有效捕捉不同语言种类之间的遗传关系。