Concept map-based multi-document summarization has recently been proposed as a variant of the traditional summarization task with graph-structured summaries.
There is thus a crucial gap between sentence selection and fusion to support summarizing by both compressing single sentences and fusing pairs.
Extracting summaries via integer linear programming and submodularity are popular and successful techniques in extractive multi-document summarization.
Coherent extracts are a novel type of summary combining the advantages of manually created abstractive summaries, which are fluent but difficult to evaluate, and low-quality automatically created extractive summaries, which lack coherence and structure.
In a detailed analysis, we show that our new corpus is significantly different from the homogeneous corpora commonly used, and that it is heterogeneous along several dimensions.