Experiment on linguistically-based term associations
Information Processing and Management: an International Journal
Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Generic text summarization using relevance measure and latent semantic analysis
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
The Theory and Practice of Discourse Parsing and Summarization
The Theory and Practice of Discourse Parsing and Summarization
On the Surprising Behavior of Distance Metrics in High Dimensional Spaces
ICDT '01 Proceedings of the 8th International Conference on Database Theory
Summarization evaluation using relative utility
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Minimizing word error rate in textual summaries of spoken language
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
The automated acquisition of topic signatures for text summarization
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Centroid-based summarization of multiple documents
Information Processing and Management: an International Journal
Topic themes for multi-document summarization
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
PageRank without hyperlinks: structural re-ranking using links induced by language models
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Semi-supervised learning with graphs
Semi-supervised learning with graphs
Language model-based document clustering using random walks
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
The Pyramid Method: Incorporating human content selection variation in summarization evaluation
ACM Transactions on Speech and Language Processing (TSLP)
CollabSum: exploiting multiple document clustering for collaborative single document summarizations
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Beyond SumBasic: Task-focused summarization with sentence simplification and lexical expansion
Information Processing and Management: an International Journal
A complex network approach to text summarization
Information Sciences: an International Journal
NAACL-ANLP-AutoSum '00 Proceedings of the 2000 NAACL-ANLP Workshop on Automatic Summarization
Mixed-source multi-document speech-to-text summarization
MMIES '08 Proceedings of the Workshop on Multi-source Multilingual Information Extraction and Summarization
LexRank: graph-based lexical centrality as salience in text summarization
Journal of Artificial Intelligence Research
Sentence compression as tree transduction
Journal of Artificial Intelligence Research
A comprehensive comparative evaluation of RST-based summarization methods
ACM Transactions on Speech and Language Processing (TSLP)
Extractive summarization of broadcast news: comparing strategies for European portuguese
TSD'07 Proceedings of the 10th international conference on Text, speech and dialogue
Term-weighting for summarization of multi-party spoken dialogues
MLMI'07 Proceedings of the 4th international conference on Machine learning for multimodal interaction
EUSUM: extracting easy-to-understand english summaries for non-native readers
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
PageRank without hyperlinks: Structural reranking using links induced by language models
ACM Transactions on Information Systems (TOIS)
Quantifying the limits and success of extractive summarization systems across domains
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
From frequency to meaning: vector space models of semantics
Journal of Artificial Intelligence Research
Recent advances in automatic speech summarization
Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
Enriching speech recognition with automatic detection of sentence boundaries and disfluencies
IEEE Transactions on Audio, Speech, and Language Processing
Self reinforcement for important passage retrieval
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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In automatic summarization, centrality-as-relevance means that the most important content of an information source, or a collection of information sources, corresponds to the most central passages, considering a representation where such notion makes sense (graph, spatial, etc.). We assess the main paradigms, and introduce a new centrality-based relevance model for automatic summarization that relies on the use of support sets to better estimate the relevant content. Geometric proximity is used to compute semantic relatedness. Centrality (relevance) is determined by considering the whole input source (and not only local information), and by taking into account the existence of minor topics or lateral subjects in the information sources to be summarized. The method consists in creating, for each passage of the input source, a support set consisting only of the most semantically related passages. Then, the determination of the most relevant content is achieved by selecting the passages that occur in the largest number of support sets. This model produces extractive summaries that are generic, and language- and domainindependent. Thorough automatic evaluation shows that the method achieves state-of-the art performance, both in written text, and automatically transcribed speech summarization, including when compared to considerably more complex approaches.