New Methods in Automatic Extracting
Journal of the ACM (JACM)
The rhetorical parsing of unrestricted texts: a surface-based approach
Computational Linguistics
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
The automated acquisition of topic signatures for text summarization
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Abstract generation based on rhetorical structure extraction
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
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
Sentence level discourse parsing using syntactic and lexical information
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Representing Discourse Coherence: A Corpus-Based Study
Computational Linguistics
Building a discourse-tagged corpus in the framework of Rhetorical Structure Theory
SIGDIAL '01 Proceedings of the Second SIGdial Workshop on Discourse and Dialogue - Volume 16
Revisions that improve cohesion in multi-document summaries: a preliminary study
AS '02 Proceedings of the ACL-02 Workshop on Automatic Summarization - Volume 4
Manual and automatic evaluation of summaries
AS '02 Proceedings of the ACL-02 Workshop on Automatic Summarization - Volume 4
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Topic-focused multi-document summarization using an approximate oracle score
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Using automatically labelled examples to classify rhetorical relations: An assessment
Natural Language Engineering
Evaluation of Automatic Text Summarization Methods Based on Rhetorical Structure Theory
ISDA '08 Proceedings of the 2008 Eighth International Conference on Intelligent Systems Design and Applications - Volume 02
LexRank: graph-based lexical centrality as salience in text summarization
Journal of Artificial Intelligence Research
SigDIAL '06 Proceedings of the 7th SIGdial Workshop on Discourse and Dialogue
Automatic sense prediction for implicit discourse relations in text
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Semi-supervised discourse relation classification with structural learning
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part I
PROPOR'12 Proceedings of the 10th international conference on Computational Processing of the Portuguese Language
Rhetorical relations for information retrieval
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
A reranking model for discourse segmentation using subtree features
SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Discourse structure and language technology
Natural Language Engineering
The effectiveness of automatic text summarization in mobile learning contexts
Computers & Education
Summarization of legal texts with high cohesion and automatic compression rate
JSAI-isAI'12 Proceedings of the 2012 international conference on New Frontiers in Artificial Intelligence
Extractive single-document summarization based on genetic operators and guided local search
Expert Systems with Applications: An International Journal
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We present analyses aimed at eliciting which specific aspects of discourse provide the strongest indication for text importance. In the context of content selection for single document summarization of news, we examine the benefits of both the graph structure of text provided by discourse relations and the semantic sense of these relations. We find that structure information is the most robust indicator of importance. Semantic sense only provides constraints on content selection but is not indicative of important content by itself. However, sense features complement structure information and lead to improved performance. Further, both types of discourse information prove complementary to non-discourse features. While our results establish the usefulness of discourse features, we also find that lexical overlap provides a simple and cheap alternative to discourse for computing text structure with comparable performance for the task of content selection.