We propose a new MDS paradigm called reader-aware multi-document
summarization (RA-MDS). Specifically, a set of reader comments associated with
the news reports are also collected. The generated summaries from the reports
for the event should be salient according to not only the reports but also the
reader comments. To tackle this RA-MDS problem, we propose a
sparse-coding-based method that is able to calculate the salience of the text
units by jointly considering news reports and reader comments. Another
reader-aware characteristic of our framework is to improve linguistic quality
via entity rewriting. The rewriting consideration is jointly assessed together
with other summarization requirements under a unified optimization model. To
support the generation of compressive summaries via optimization, we explore a
finer syntactic unit, namely, noun/verb phrase. In this work, we also generate
a data set for conducting RA-MDS. Extensive experiments on this data set and
some classical data sets demonstrate the effectiveness of our proposed
approach.

提出了一种新的 MDS 范例 —— 读者感知式多文档摘要（RA-MDS），通过特定的稀疏编码法联合考虑新闻报道和读者评论来计算文本片段的显著性，并通过实体重写来提高语言质量，进一步探讨了名词 / 动词短语的优化模型支持压缩摘要的生成，该方法在经典和自行采集的数据集上取得了显著的效果