Proceedings of the ACM SIGCHI Conference on Human factors in computing systems
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Information fusion in the context of multi-document summarization
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Automatic evaluation of summaries using N-gram co-occurrence statistics
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Extracting knowledge from evaluative text
Proceedings of the 3rd international conference on Knowledge capture
NAACL-ANLP-AutoSum '00 Proceedings of the 2000 NAACL-ANLPWorkshop on Automatic summarization - Volume 4
A strategy for generating evaluative arguments
INLG '00 Proceedings of the first international conference on Natural language generation - Volume 14
The Pyramid Method: Incorporating human content selection variation in summarization evaluation
ACM Transactions on Speech and Language Processing (TSLP)
Generating and evaluating evaluative arguments
Artificial Intelligence
A multimedia interface for facilitating comparisons of opinions
Proceedings of the 14th international conference on Intelligent user interfaces
Data quality from crowdsourcing: a study of annotation selection criteria
HLT '09 Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing
Sentiment summarization: evaluating and learning user preferences
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Optimization-based content selection for opinion summarization
UCNLG+Sum '09 Proceedings of the 2009 Workshop on Language Generation and Summarisation
Optimizing informativeness and readability for sentiment summarization
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Eliminating redundancy by spectral relaxation for multi-document summarization
TextGraphs-5 Proceedings of the 2010 Workshop on Graph-based Methods for Natural Language Processing
Generating and validating abstracts of meeting conversations: a user study
INLG '10 Proceedings of the 6th International Natural Language Generation Conference
COMPENDIUM: a text summarization system for generating abstracts of research papers
NLDB'11 Proceedings of the 16th international conference on Natural language processing and information systems
Persuasive language and virality in social networks
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
Text summarisation in progress: a literature review
Artificial Intelligence Review
Micropinion generation: an unsupervised approach to generating ultra-concise summaries of opinions
Proceedings of the 21st international conference on World Wide Web
AUSUM: approach for unsupervised bug report summarization
Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering
Editorial: COMPENDIUM: A text summarization system for generating abstracts of research papers
Data & Knowledge Engineering
| Hi-index | 0.00 |
Extractive summarization is the strategy of concatenating extracts taken from a corpus into a summary, while abstractive summarization involves paraphrasing the corpus using novel sentences. We define a novel measure of corpus controversiality of opinions contained in evaluative text, and report the results of a user study comparing extractive and NLG-based abstractive summarization at different levels of controversiality. While the abstractive summarizer performs better overall, the results suggest that the margin by which abstraction outperforms extraction is greater when controversiality is high, providing a context in which the need for generation-based methods is especially great.