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
Towards multidocument summarization by reformulation: progress and prospects
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
The rhetorical parsing of unrestricted texts: a surface-based approach
Computational Linguistics
Generating natural language summaries from multiple on-line sources
Computational Linguistics - Special issue on natural language generation
Empirically estimating order constraints for content planning in generation
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Inferring temporal ordering of events in news
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Probabilistic text structuring: experiments with sentence ordering
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Robust temporal processing of news
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Assigning time-stamps to event-clauses
TASIP '01 Proceedings of the workshop on Temporal and spatial information processing - Volume 13
Improving chronological sentence ordering by precedence relation
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Automatic Evaluation of Information Ordering: Kendall's Tau
Computational Linguistics
Sentence ordering with manifold-based classification in multi-document summarization
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Measuring variability in sentence ordering for news summarization
ENLG '07 Proceedings of the Eleventh European Workshop on Natural Language Generation
Learning sentence-internal temporal relations
Journal of Artificial Intelligence Research
Inferring strategies for sentence ordering in multidocument news summarization
Journal of Artificial Intelligence Research
From local to global coherence: a bottom-up approach to text planning
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Multimedia news exploration and retrieval by integrating keywords, relations and visual features
Multimedia Tools and Applications
A preference learning approach to sentence ordering for multi-document summarization
Information Sciences: an International Journal
Multiple documents summarization based on evolutionary optimization algorithm
Expert Systems with Applications: An International Journal
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Ordering information is a difficult but important task for applications generating natural language texts such as multi-document summarization, question answering, and concept-to-text generation. In multi-document summarization, information is selected from a set of source documents. However, improper ordering of information in a summary can confuse the reader and deteriorate the readability of the summary. Therefore, it is vital to properly order the information in multi-document summarization. We present a bottom-up approach to arrange sentences extracted for multi-document summarization. To capture the association and order of two textual segments (e.g. sentences), we define four criteria: chronology, topical-closeness, precedence, and succession. These criteria are integrated into a criterion by a supervised learning approach. We repeatedly concatenate two textual segments into one segment based on the criterion, until we obtain the overall segment with all sentences arranged. We evaluate the sentence orderings produced by the proposed method and numerous baselines using subjective gradings as well as automatic evaluation measures. We introduce the average continuity, an automatic evaluation measure of sentence ordering in a summary, and investigate its appropriateness for this task.