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- Ahlgren, Per, et al.
(författare)
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Indexing strategies for Swedish full text retrieval under different user scenarios
- 2007
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Ingår i: Information Processing & Management. - : Elsevier BV. - 0306-4573 .- 1873-5371. ; 43:1, s. 81-102
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Tidskriftsartikel (refereegranskat)abstract
- This paper deals with Swedish full text retrieval and the problem of morphological variation of query terms in the document database. The effects of combination of indexing strategies with query terms on retrieval effectiveness were studied. Three of five tested combinations involved indexing strategies that used conflation, in the form of normalization. Further, two of these three combinations used indexing strategies that employed compound splitting. Normalization and compound splitting were performed by SWETWOL, a morphological analyzer for the Swedish language. A fourth combination attempted to group related terms by right hand truncation of query terms. The four combinations were compared to each other and to a baseline combination, where no attempt was made to counteract the problem of morphological variation of query terms in the document database. The five combinations were evaluated under six different user scenarios, where each scenario simulated a certain user type. The four alternative combinations outperformed the baseline, for each user scenario. The truncation combination had the best performance under each user scenario. The main conclusion of the paper is that normalization and right hand truncation (performed by a search expert) enhanced retrieval effectiveness in comparison to the baseline. The performance of the three combinations of indexing strategies with query terms based on normalization was not far below the performance of the truncation combination. (c) 2006 Elsevier Ltd. All rights reserved.
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- Sjöbergh, Jonas
(författare)
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Older versions of the ROUGEeval summarization evaluation system were easier to fool
- 2007
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Ingår i: Information Processing & Management. - : Elsevier BV. - 0306-4573 .- 1873-5371. ; 43:6, s. 1500-1505
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Tidskriftsartikel (refereegranskat)abstract
- We show some limitations of the ROUGE evaluation method for automatic summarization. We present a method for automatic summarization based on a Markov model of the source text. By a simple greedy word selection strategy, summaries with high ROUGE-scores are generated. These summaries would however not be considered good by human readers. The method can be adapted to trick different settings of the ROUGEeval package.
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