A bottom-up approach to sentence ordering for multi-document summarization

Danushka Bollegala*, Naoaki Okazaki, Mitsuru Ishizuka

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

43 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)89-109
Number of pages21
JournalInformation Processing and Management
Issue number1
Publication statusPublished - 2010 Jan
Externally publishedYes


  • Multi-document summarization
  • Natural language processing
  • Sentence ordering

ASJC Scopus subject areas

  • Media Technology
  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences
  • Management Science and Operations Research


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