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https://doi.org/10.3115/v1/p14...
Article . 2014 . Peer-reviewed
Data sources: Crossref
DBLP
Conference object . 2021
Data sources: DBLP
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Hierarchical Summarization: Scaling Up Multi-Document Summarization

Authors: Janara Christensen; Stephen Soderland; Gagan Bansal; Mausam;

Hierarchical Summarization: Scaling Up Multi-Document Summarization

Abstract

Multi-document summarization (MDS) systems have been designed for short, unstructured summaries of 10-15 documents, and are inadequate for larger document collections. We propose a new approach to scaling up summarization called hierarchical summarization, and present the first implemented system, SUMMA. SUMMA produces a hierarchy of relatively short summaries, in which the top level provides a general overview and users can navigate the hierarchy to drill down for more details on topics of interest. SUMMA optimizes for coherence as well as coverage of salient information. In an Amazon Mechanical Turk evaluation, users prefered SUMMA ten times as often as flat MDS and three times as often as timelines.

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
12
Top 10%
Top 10%
Average