Automatic Tailored Multi-Paper Summarization based on Rhetorical Document Profile and Summary Specification

Masayu Leylia Khodra; Dwi Hendratmo Widyantoro; E. Aminudin Aziz; Bambang Riyanto Trilaksono

doi:10.5614/itbj.ict.2012.6.3.4

Automatic Tailored Multi-Paper Summarization based on Rhetorical Document Profile and Summary Specification

Published: 2012-01-01 | DOI: 10.5614/itbj.ict.2012.6.3.4 | Vol 6, Issue 3 (2012) | Cited by: 8

Authors

Masayu Leylia Khodra

School of Electrical Engineering and Informatics, Bandung Institute of Technology, Jalan Ganesa No.10, Bandung 40132

ORCID: 0000-0002-8765-8109 · OpenAlex: A5031833654

Dwi Hendratmo Widyantoro

School of Electrical Engineering and Informatics, Bandung Institute of Technology, Jalan Ganesa No.10, Bandung 40132

ORCID: 0000-0002-8718-5604 · OpenAlex: A5010847400

E. Aminudin Aziz

Faculty of Language and Arts Education, Indonesia University of Education, Jalan Dr. Setiabudhi No. 229 Bandung

OpenAlex: A5110988484

Bambang Riyanto Trilaksono

School of Electrical Engineering and Informatics, Bandung Institute of Technology, Jalan Ganesa No.10, Bandung 40132

ORCID: 0000-0002-1149-1832 · OpenAlex: A5053775542

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Abstract

In order to assist researchers in addressing time constraint and low relevance in using scientific articles, an automatic tailored multi-paper summarization (TMPS) is proposed. In this paper, we extend Teufel

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Research Intelligence

Data from OpenAlex ↗

Metrics

Citations

0.48

FWCIfield-weighted

77th

Percentilevs same year + field

Article

Work type

GOLD

Open Access

Topics

Advanced Text Analysis Techniques Primary

1.00

Artificial Intelligence › Computer Science › Physical Sciences

Topic Modeling

1.00

Natural Language Processing Techniques

1.00

Related Research

Citation Trend

2015

2016

2017

2018

2019

2020

Citation Timeline

Year	Citations
2020	2
2019	1
2018	2
2017	1
2016	1
2015	1

Semantic Profile AI-classified research signals

Core Domains

Automatic summarization 0.90

level 2

Readability 0.85

level 2

Computer science 0.84

level 0

Secondary Topics

Generality 0.80 Relevance (law) 0.68 Rhetorical question 0.56 Information retrieval 0.54 Constraint (computer-aided design) 0.51 Task (project management) 0.49 Quality (philosophy) 0.43 Artificial intelligence 0.42 Natural language processing 0.39 Software engineering 0.32

Institution Network

Indonesia University of Education ID
Masayu Leylia Khodra · E. Aminudin Aziz
Bandung Institute of Technology ID
Masayu Leylia Khodra · Dwi H. Widyantoro · Bambang Riyanto Trilaksono
Informatics Institute of Technology LK
Masayu Leylia Khodra · Dwi H. Widyantoro · Bambang Riyanto Trilaksono

References

Maxie, G., Critical Writing and Reading of Review Articles, The Canadian Veterinary Journal, 31(6), pp.413-414, 1990.
Torraco, R.J., Writing Integrative Literature Reviews: Guidelines and Examples, Human Resource Development Review, 4(3), 356-367, 2005.
Mohammad, S., Dorr, B., Egan, M., Hassan, A., Muthukrishan, P., Qazvinian, V., Radev, D. & Zajic, D., Using Citations to Generate Surveys of Scientific Paradigms, in Proc. of HLT/ NAACL 2009.
Wang, M., Tanaka, H. & Zhong, Y., Generating Summaries of Multiple Technical Articles,in Proc. of Sino-Japan Symposium on IIN, 2000.
Fiszman, M. & Rindflesch, T.C., Abstraction Summarization for Managing the Biomedical Research Literature, in Proc. of HLT/NAACL 2004.
Jiaming, Z. Exploiting Textual Structures of Technical Papers for Automatic Multi-Document Summarization, PhD Thesis, NUS, 2008
Shiyan, O., Khoo, C.S.G. & Goh, D.H., Design and Development of A Concept-based Multi Document Summarization System for Research Abstracts, Journal of Information Science, 34, pp.308-326, 2008.
Saracevic, T., Relevance: A Review of the Literature and a Framework for Thinking on the Notion in Information Science. Part III: Behavior and Effects of Relevance, Journal of The American Society For Information Science And Technology, 58, pp. 2126-2144, 2007.
Borlund, P., The Concept of Relevance, Journal of The American Society for Information Science and Technology, 54, pp. 913-925, 2003.
Teufel, S., Argumentative Zoning: Information Extraction from Scientific Text, PhD Dissertation, University of Edinburgh, 1999.
Teufel, S., Siddhartan, A. & Batchelor, C., Towards DisciplineIndependent Argumentative zoning Evidence from Chemistry and Computational linguistics, in Proc. of Conference on Empirical Methods in NLP 2009.
Jones, K. S., Automatic Summarising: The State of The Art, Information Processing and Management, 43, pp.1449-1481, 2007.
Marcu, D., Automatic Abstracting, Encyclopedia of Library and Information Science, pp.245-256, 2003.
Gupta, V. & Lehal, G.S., A Survey of Text Summarization Extractive Techniques, Journal of Emerging Technologies In Web Intelligence, 2(3), 2010.
Radev, D. R., Hovy, E. & McKeown, K., Introduction to the Special Issue on Summarization, Journal Computational Linguistics- Summarization, 28(4), 2002.
Agarwal, N. & Gvr, K., Towards Multi-Document Summarization of Scientific Articles:Making Interesting Comparisons with SciSumm, in Proc. of the Workshop on Automatic Summarization ACL 2011.
Bird, S., Dale, R., Dorr, B.J. & Gibson, B., The ACL Anthology Reference Corpus: A Reference Dataset for Bibliographic Research in Computational linguistics, in Proc. of Language Resources and Evaluation Conference 2008.
PDFBox, http://pdfbox.apache.org/,(November 2011).
Luebbert, D. L., Method and System for Handling Text That Includes Paragraph Delimiters of Differing Formats, US Patent June 1996.
OpenNLP, http://opennlp.sourceforge.net, (April 2011).
Reynar, J.C. & Ratnaparkhi, A., A Maximum Entropy Approach to Identifying Sentence Boundaries, in Proc. of the 5th Conference on Applied Natural Language Processing 1997.
Hyland, K. & Tse, P., Metadiscourse in Academic Writing: A Reappraisal, Applied Linguistics, 25, pp.156-177, 2004.
Hsu, C.W., Chang, C.C. & Lin, C.J., A Practical Guide to Support Vector Classification, www.csie.ntu.edu.tw/~cjlin/papers/guide/, (December 2009).
Sun, A., Lim, E.P. & Liu, Y., On Strategies for Imbalanced Text Classification Using SVM: A Comparative Study, DSS Elsevier 2009.
Chau M., Chen, H., A Machine Learning Approach to Web Page Filtering Using Content and Structure Analysis, DSS Elsevier, 44, pp. 482-494, 2008.
Sun, A., Lim, E.P. & Ng, W.K., Performance Measurement Framework for Hierarchical Text Classification, Journal of The American Society For Information Science And Technology, 54, pp. 1014-1028, 2003.
Zhang, Y., Dang, Y., Chen, H., Thurmond, M. &Larson, C., Automatic Online News Monitoring and Classification for Syndromic Surveillance, DSS Elsevier 2009.
Hsu, C.W. & Lin, C.J., A Comparison of Methods for Multiclass Support Vector Machines, IEEE Transactions On Neural Networks, 13, pp.415-425, 2002.
Rifkin, R. & Klautau, A., In Defense of One-Vs-All Classication, Journal of Machine Learning Research, 5, pp.101-141, 2004.
Allwein, E.L., Schapire, R.E. & Singer, Y., Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers, Journal of Machine Learning Research, 1, pp. 113-141, 2000.
Hinkel, E., Tense, Aspect and The Passive Voice in L1 And L2 Academic Texts, Language Teaching Research, 8, pp. 5-29, 2004.
WordNet: Standoff Files, http://wordnet.princeton.edu,(April 2012).
Fellbaum, C., Osherson, A. & Clark, P.E., Putting Semantics into WordNet
Carbonell, J., Goldstein, J., The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries, in Proc. of SIGIR 1998.
Khodra, M.L., Widyantoro, D.H., Aziz, E.A. & Trilaksono, B.R., Information Extraction for Scientific Paper Using Rhetorical Classifier, in Proc. of ICEEI 2011.
Gorrell, G., Ford, N., Madden, A., Holdridge, P. & Eadlestone, B., Countering Method Bias in Questionnaire-Based User Studies, Journal of Documentation, 67, pp.507-524, 2011.

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[ref-1] Maxie, G., Critical Writing and Reading of Review Articles, The Canadian Veterinary Journal, 31(6), pp.413-414, 1990.

[ref-2] Torraco, R.J., Writing Integrative Literature Reviews: Guidelines and Examples, Human Resource Development Review, 4(3), 356-367, 2005.

[ref-3] Mohammad, S., Dorr, B., Egan, M., Hassan, A., Muthukrishan, P., Qazvinian, V., Radev, D. & Zajic, D., Using Citations to Generate Surveys of Scientific Paradigms, in Proc. of HLT/ NAACL 2009.

[ref-4] Wang, M., Tanaka, H. & Zhong, Y., Generating Summaries of Multiple Technical Articles,in Proc. of Sino-Japan Symposium on IIN, 2000.

[ref-5] Fiszman, M. & Rindflesch, T.C., Abstraction Summarization for Managing the Biomedical Research Literature, in Proc. of HLT/NAACL 2004.

[ref-6] Jiaming, Z. Exploiting Textual Structures of Technical Papers for Automatic Multi-Document Summarization, PhD Thesis, NUS, 2008

[ref-7] Shiyan, O., Khoo, C.S.G. & Goh, D.H., Design and Development of A Concept-based Multi Document Summarization System for Research Abstracts, Journal of Information Science, 34, pp.308-326, 2008.

[ref-8] Saracevic, T., Relevance: A Review of the Literature and a Framework for Thinking on the Notion in Information Science. Part III: Behavior and Effects of Relevance, Journal of The American Society For Information Science And Technology, 58, pp. 2126-2144, 2007.

[ref-9] Borlund, P., The Concept of Relevance, Journal of The American Society for Information Science and Technology, 54, pp. 913-925, 2003.

[ref-10] Teufel, S., Argumentative Zoning: Information Extraction from Scientific Text, PhD Dissertation, University of Edinburgh, 1999.

[ref-11] Teufel, S., Siddhartan, A. & Batchelor, C., Towards DisciplineIndependent Argumentative zoning Evidence from Chemistry and Computational linguistics, in Proc. of Conference on Empirical Methods in NLP 2009.

[ref-12] Jones, K. S., Automatic Summarising: The State of The Art, Information Processing and Management, 43, pp.1449-1481, 2007.

[ref-13] Marcu, D., Automatic Abstracting, Encyclopedia of Library and Information Science, pp.245-256, 2003.

[ref-14] Gupta, V. & Lehal, G.S., A Survey of Text Summarization Extractive Techniques, Journal of Emerging Technologies In Web Intelligence, 2(3), 2010.

[ref-15] Radev, D. R., Hovy, E. & McKeown, K., Introduction to the Special Issue on Summarization, Journal Computational Linguistics- Summarization, 28(4), 2002.

[ref-16] Agarwal, N. & Gvr, K., Towards Multi-Document Summarization of Scientific Articles:Making Interesting Comparisons with SciSumm, in Proc. of the Workshop on Automatic Summarization ACL 2011.

[ref-17] Bird, S., Dale, R., Dorr, B.J. & Gibson, B., The ACL Anthology Reference Corpus: A Reference Dataset for Bibliographic Research in Computational linguistics, in Proc. of Language Resources and Evaluation Conference 2008.

[ref-18] PDFBox, http://pdfbox.apache.org/,(November 2011).

[ref-19] Luebbert, D. L., Method and System for Handling Text That Includes Paragraph Delimiters of Differing Formats, US Patent June 1996.

[ref-20] OpenNLP, http://opennlp.sourceforge.net, (April 2011).

[ref-21] Reynar, J.C. & Ratnaparkhi, A., A Maximum Entropy Approach to Identifying Sentence Boundaries, in Proc. of the 5th Conference on Applied Natural Language Processing 1997.

[ref-22] Hyland, K. & Tse, P., Metadiscourse in Academic Writing: A Reappraisal, Applied Linguistics, 25, pp.156-177, 2004.

[ref-23] Hsu, C.W., Chang, C.C. & Lin, C.J., A Practical Guide to Support Vector Classification, www.csie.ntu.edu.tw/~cjlin/papers/guide/, (December 2009).

[ref-24] Sun, A., Lim, E.P. & Liu, Y., On Strategies for Imbalanced Text Classification Using SVM: A Comparative Study, DSS Elsevier 2009.

[ref-25] Chau M., Chen, H., A Machine Learning Approach to Web Page Filtering Using Content and Structure Analysis, DSS Elsevier, 44, pp. 482-494, 2008.

[ref-26] Sun, A., Lim, E.P. & Ng, W.K., Performance Measurement Framework for Hierarchical Text Classification, Journal of The American Society For Information Science And Technology, 54, pp. 1014-1028, 2003.

[ref-27] Zhang, Y., Dang, Y., Chen, H., Thurmond, M. &Larson, C., Automatic Online News Monitoring and Classification for Syndromic Surveillance, DSS Elsevier 2009.

[ref-28] Hsu, C.W. & Lin, C.J., A Comparison of Methods for Multiclass Support Vector Machines, IEEE Transactions On Neural Networks, 13, pp.415-425, 2002.

[ref-29] Rifkin, R. & Klautau, A., In Defense of One-Vs-All Classication, Journal of Machine Learning Research, 5, pp.101-141, 2004.

[ref-30] Allwein, E.L., Schapire, R.E. & Singer, Y., Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers, Journal of Machine Learning Research, 1, pp. 113-141, 2000.

[ref-31] Hinkel, E., Tense, Aspect and The Passive Voice in L1 And L2 Academic Texts, Language Teaching Research, 8, pp. 5-29, 2004.

[ref-32] WordNet: Standoff Files, http://wordnet.princeton.edu,(April 2012).

[ref-33] Fellbaum, C., Osherson, A. & Clark, P.E., Putting Semantics into WordNet

[ref-34] Carbonell, J., Goldstein, J., The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries, in Proc. of SIGIR 1998.

[ref-35] Khodra, M.L., Widyantoro, D.H., Aziz, E.A. & Trilaksono, B.R., Information Extraction for Scientific Paper Using Rhetorical Classifier, in Proc. of ICEEI 2011.

[ref-36] Gorrell, G., Ford, N., Madden, A., Holdridge, P. & Eadlestone, B., Countering Method Bias in Questionnaire-Based User Studies, Journal of Documentation, 67, pp.507-524, 2011.