ACM Home Page
Please provide us with feedback. Feedback
Interactive multimedia summaries of evaluative text
Full text PdfPdf (727 KB)
Source International Conference on Intelligent User Interfaces archive
Proceedings of the 11th international conference on Intelligent user interfaces table of contents
Sydney, Australia
SESSION: Multimedia and multimodality table of contents
Pages: 124 - 131  
Year of Publication: 2006
ISBN:1-59593-287-9
Authors
Giuseppe Carenini  University of British Columbia, Vancouver, B.C. Canada
Raymond T. Ng  University of British Columbia, Vancouver, B.C. Canada
Adam Pauls  University of British Columbia, Vancouver, B.C. Canada
Sponsors
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
ACM: Association for Computing Machinery
SIGART: ACM Special Interest Group on Artificial Intelligence
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 12,   Downloads (12 Months): 79,   Citation Count: 3
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1111449.1111480
What is a DOI?

ABSTRACT

We present an interactive multimedia interface for automatically summarizing large corpora of evaluative text (e.g. online product reviews). We rely on existing techniques for extracting knowledge from the corpora but present a novel approach for conveying that knowledge to the user. Our system presents the extracted knowledge in a hierarchical visualization mode as well as in a natural language summary. We propose a method for reasoning about the extracted knowledge so that the natural language summary can include only the most important information from the corpus. Our approach is interactive in that it allows the user to explore in the original dataset through intuitive visual and textual methods. Results of a formative evaluation of our interface show general satisfaction among users with our approach.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
 
2
D. Brodbeck and L. Girardin. Visualization of large-scale customer satisfacyion surveys using a parallel coordinate tree. In Proc. of the IEEE Symposium on Information Visualization, 2003.
 
3
 
4
G. Carenini and J. D. Moore. An empirical study of the influence of user tailoring on evaluative argument effectiveness. In Proceedings of the 17th International Joint Conference on Artificial Intelligence, Seattle, USA, 2001.
5
 
6
 
7
H. Guo and A. J. Stent. Trainable adaptable multimedia presentation generation. In Proceedings of the International conferences on Multimodal Interfaces, Trento, Italy, 2005.
 
8
M. Hu and B. Liu. Feature based summary of customer reviews dataset. http://www.cs.uic.edu/~liub/FBS/FBS.html, 2004.
9
 
10
M. Hu and B. Liu. Mining opinion features in customer reviews. In Proc. AAAI, 2004.
 
11
 
12
D. Kusui, K. Tateishi, and T. Fukoshima. Information extraction and visualization from internet documents. NEC Journal of Advanced technology, 2(2):157--163, 2005.
 
13
 
14
15
 
16
Guidelines of the 2004 document understanding conference. http://www-nlpir.nist.gov/projects/duc/guidelines/2004.html, 2004.
 
17
Online proceedings of the 2004 document understanding conference. http://duc.nist.gov/pubs.html\#2004, 2004.
18
 
19
C. Plaisant, B. Shneiderman, G. Chintalapani, and A. Aris. Treemap home page. http://www.cs.umd.edu/hcil/treemap/.
 
20
 
21
 
22
H. Saggion and R. Gaizauskas. Multi-document summarization by cluster/profile relevance and redundancy removal. In Proceedings of Document Understanding Conference DUC04, 2004.
 
23
B. Schiffman, A. Nenkova, and K. McKeown. Experiments in multidocument summarization. In Proceedings of Human Language Technology HLT02, San Diego, Ca., 2002.
24
 
25
 
26
J. Yu, E. Reiter, J. Hunter, and C. Mellish. Choosing the content of textual summaries of large time-series data sets. Natural Language Engineering, to appear, 2005.


Collaborative Colleagues:
Giuseppe Carenini: colleagues
Raymond T. Ng: colleagues
Adam Pauls: colleagues