Query-based open-domain NLP tasks require information synthesis from long and diverse web results.
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.
Generating a text abstract from a set of documents remains a challenging task.
We show that generating English Wikipedia articles can be approached as a multi- document summarization of source documents.
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.