Using linguistically motivated features for paragraph boundary identification

Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP 2006), pages 267–274, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Using Linguistically Motivated Features for Paragraph Boundary Identification Katja Filippova and Michael Strube EML Research gGmbH Schloss-Wolfsbrunnenweg 33 69118 Heidelberg, Germany http://www.eml-research.de/nlp Abstract In this paper we propose a machine- learning approach to paragraph boundary identification which utilizes linguistically motivated features. We investigate the re- lation between paragraph boundaries and discourse cues, pronominalization and in- formation structure. We test our algorithm on German data and report improvements over three baselines including a reimple- mentation of Sporleder & Lapata’s (2006) work on paragraph segmentation. An analysis of the features’ contribution sug- gests an interpretation of what paragraph boundaries indicate and what they depend on. 1 Introduction Our work is concerned with multi-document sum- marization, namely with the merging of multiple documents about the same topic taken from the web. We view summarization as extraction of im- portant sentences from the text. As a consequence of the merging process the layout of the documents is lost. In order to create the layout of the out- put, the document structure (Power et al., 2003) has to be regenerated. One aspect of this struc- ture is of particular importance for our work: the paragraph structure. In web documents paragraph boundaries are used to anchor figures and illustra- tions, so that the figures are always aligned with the same paragraph even when the font size or the window size is changed. Since we want to include figures in the generated summaries, paragraph seg- mentation is an important subtask in our applica- tion. Besides multi-document summarization of web documents, paragraph boundary identification (PBI) could be useful for a number of different ap- plications, such as producing the layout for tran- scripts provided by speech recognizers and opti- cal character recognition systems, and determin- ing the layout of documents generated for output devices with different screen size. Though related to the task of topic segmenta- tion which stimulated a large number of studies (Hearst, 1997; Choi, 2000; Galley et al., 2003, inter alia), paragraph segmentation has not been thoroughly investigated so far. We explain this by the fact that paragraphs are considered a stylistic phenomenon and that there is no unanimous opin- ion on what the function of the paragraph is. Some authors (Irmscher (1972) as cited by Stark (1988)) suggest that paragraph structure is arbitrary and can not be determined based solely on the prop- erties of the text. Still, psycholinguistic studies report that humans agree, at least to some extent, on placing boundaries between paragraphs. These studies also note that paragraph boundaries are in- formative and make the reader perceive paragraph- initial sentences as being important (Stark, 1988). In contrast to topic segmentation, paragraph seg- mentation has the advantage that large amounts of annotated data are readily availabe for supervised learning. In this paper we describe our approach to para- graph segmentation. Previous work (Sporleder & Lapata, 2004; 2006) mainly focused …

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