Publication Type
Journal Article
Version
publishedVersion
Publication Date
9-2017
Abstract
Location-based social networks (LBSNs), exemplified by Foursquare, are fast gaining popularity. One important feature of LBSNs is micro-review. Upon check-in at a particular venue, a user may leave a short review (up to 200 characters long), also known as a tip. These tips are an important source of information for others to know more about various aspects of an entity (e.g., restaurant), such as food, waiting time, or service. However, a user is often interested not in one particular entity, but rather in several entities collectively, for instance within a neighborhood or a category. In this paper, we address the problem of summarizing the tips of multiple entities in a collection, by way of synthesizing new micro-reviews that pertain to the collection, rather than to the individual entities per se. We formulate this problem in terms of first finding a representation of the collection, by identifying a number of “aspects” that link common threads across two or more entities within the collection. We express these aspects as dense subgraphs in a graph of sentences derived from the multi-entity corpora. This leads to a formulation of maximal multi-entity quasi-cliques, as well as a heuristic algorithm to find K such quasi-cliques maximizing the coverage over the multi-entity corpora. To synthesize a summary tip for each aspect, we select a small number of sentences from the corresponding quasi-clique, balancing conciseness and representativeness in terms of a facility location problem. Our approach performs well on collections of Foursquare entities based on localities and categories, producing more representative and diverse summaries than the baselines.
Keywords
Maximal quasi-clique, Micro-review synthesis, Multi-entity summarization
Discipline
Databases and Information Systems | Theory and Algorithms
Research Areas
Data Science and Engineering
Publication
Data Mining and Knowledge Discovery
Volume
31
Issue
5
First Page
1189
Last Page
1217
ISSN
1384-5810
Identifier
10.1007/s10618-017-0491-4
Publisher
Springer Verlag (Germany)
Citation
NGUYEN, Thanh-Son; LAUW, Hady W.; and TSAPARAS, Panayiotis.
Micro-review synthesis for multi-entity summarization. (2017). Data Mining and Knowledge Discovery. 31, (5), 1189-1217.
Available at: https://ink.library.smu.edu.sg/sis_research/3467
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/s10618-017-0491-4