A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
New Methods in Automatic Extracting
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
Text summarization via hidden Markov models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Identifying topics by position
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Abstract generation based on rhetorical structure extraction
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Centroid-based summarization of multiple documents
Information Processing and Management: an International Journal
Text summarization using a trainable summarizer and latent semantic analysis
Information Processing and Management: an International Journal - Special issue: An Asian digital libraries perspective
Automatic summarising: The state of the art
Information Processing and Management: an International Journal
QCS: A system for querying, clustering and summarizing documents
Information Processing and Management: an International Journal
GA, MR, FFNN, PNN and GMM based models for automatic text summarization
Computer Speech and Language
Text summarization with harmony search algorithm-based sentence extraction
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Automatic generic document summarization based on non-negative matrix factorization
Information Processing and Management: an International Journal
Expert Systems with Applications: An International Journal
A survey of Web clustering engines
ACM Computing Surveys (CSUR)
Swarm Based Text Summarization
IACSIT-SC '09 Proceedings of the 2009 International Association of Computer Science and Information Technology - Spring Conference
Extractive summarization using supervised and semi-supervised learning
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Performance evaluation of density-based clustering methods
Information Sciences: an International Journal
Document summarization using conditional random fields
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Comparing parameter tuning methods for evolutionary algorithms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
iRANK: A rank-learn-combine framework for unsupervised ensemble ranking
Journal of the American Society for Information Science and Technology
Fuzzy swarm diversity hybrid model for text summarization
Information Processing and Management: an International Journal
A new approach to improving multilingual summarization using a genetic algorithm
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Towards a unified approach to simultaneous single-document and multi-document summarizations
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Discourse indicators for content selection in summarization
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Expert Systems with Applications: An International Journal
Text summarisation in progress: a literature review
Artificial Intelligence Review
Graph based single document summarization
ICDEM'10 Proceedings of the Second international conference on Data Engineering and Management
An improved evolutionary algorithm for extractive text summarization
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
Hi-index | 12.05 |
Due to the exponential growth of textual information available on the Web, end users need to be able to access information in summary form - and without losing the most important information in the document when generating the summaries. Automatic generation of extractive summaries from a single document has traditionally been given the task of extracting the most relevant sentences from the original document. The methods employed generally allocate a score to each sentence in the document, taking into account certain features. The most relevant sentences are then selected, according to the score obtained for each sentence. These features include the position of the sentence in the document, its similarity to the title, the sentence length, and the frequency of the terms in the sentence. However, it has still not been possible to achieve a quality of summary that matches that performed by humans and therefore methods continue to be brought forward that aim to improve on the results. This paper addresses the generation of extractive summaries from a single document as a binary optimization problem where the quality (fitness) of the solutions is based on the weighting of individual statistical features of each sentence - such as position, sentence length and the relationship of the summary to the title, combined with group features of similarity between candidate sentences in the summary and the original document, and among the candidate sentences of the summary. This paper proposes a method of extractive single-document summarization based on genetic operators and guided local search, called MA-SingleDocSum. A memetic algorithm is used to integrate the own-population-based search of evolutionary algorithms with a guided local search strategy. The proposed method was compared with the state of the art methods UnifiedRank, DE, FEOM, NetSum, CRF, QCS, SVM, and Manifold Ranking, using ROUGE measures on the datasets DUC2001 and DUC2002. The results showed that MA-SingleDocSum outperforms the state of the art methods.