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DAISY Dutch lAnguage Investigation of Summarization technologY

DAISY Dutch lAnguage Investigation of Summarization technologY. Katholieke Universiteit Leuven Rijksuniversiteit Groningen Q-go. DAISY on one slide. Segmentation Rhetorical classification Sentence compression Sentence generation. Summarization of web content.

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DAISY Dutch lAnguage Investigation of Summarization technologY

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  1. DAISYDutch lAnguage Investigationof Summarization technologY KatholiekeUniversiteit Leuven Rijksuniversiteit Groningen Q-go

  2. DAISY on one slide Segmentation Rhetorical classification Sentence compression Sentence generation Summarization of web content Improvement question answering, e.g. e-mail answering Multi-document summarization: Detect differences

  3. Overview Report of our current progress in: • Corpus building and preprocessing • Segmentation • Sentence generation

  4. Corpus Building and Preprocessing Target: corpus of questions, short texts and webpages about the same topic • Freely available: • UWV (questions & answer texts) • SVB (questions) • Available for internal use: KLM (questions, answer texts, web pages) • Todo: • web pages SVB • ABN AMRO (committed,not delivered)

  5. Corpus Building and Preprocessing • POS-tagged and parsed: KLM and UWV • SVB corpus: in progress • Coreference resolution: in progress

  6. Segmentation • Find main content in webpage • Smaller segments • Can be obtained from HTML structure • <H#>, <P>, <BR>, <UL>, ... • Hierarchical • Will be refined in relation to rhetorical roles

  7. Segmentation

  8. Segmentation

  9. Segmentation • Search for block with highest density of text

  10. Segmentation

  11. Segmentation • Additional heuristics to extend the selection: • Find closing tags for all tags that were opened in the selection • Include all text delimited by known tag patterns occurring just before and after the selection • Take the smallest enclosing DIV block

  12. Sentence generation • Specification of abstract dependency trees • Specify grammatical relations between lexical items and constituents dominating over lexical items • Alpino dependency trees without adjacency information • More variation through underspecification in lexical items, handling of particles

  13. Sentence generation • Initial implementation generator: • Chart generator (Kay, 1996) • Top-down guidance through expected dependency relations • Generates substantial part of input created from the Alpinotestsuites • Included in recent Alpino versions • Further work: optimization (time and space)

  14. Sentence generation • Selecting the most fluent sentence through fluency ranking: • N-gram language model • Log-linear model • Experiments with Velldall (2007) and parse disambiguation feature templates. • Need more insight about feature overlap • Experiment with more feature templates

  15. Sentence generation • Evaluation: • Corpus sentences used as a reference for the most fluent realization • Fairly strict, since there can be multiple fluent sentences • Where is the ceiling? • More annotated material! • FLAN: FLuencyANnotator (web application)

  16. Thanks!

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