FastSum
| Autor: | Ravi Kondadadi, Frank Schilder |
|---|---|
| Rok vydání: | 2008 |
| Předmět: |
Information retrieval
Parsing Computer science business.industry Part of speech computer.software_genre Automatic summarization Set (abstract data type) Support vector machine Ranking Multi-document summarization ComputingMethodologies_DOCUMENTANDTEXTPROCESSING Artificial intelligence business computer Natural language processing |
| Zdroj: | ACL (Short Papers) |
| Popis: | We present a fast query-based multi-document summarizer called FastSum based solely on word-frequency features of clusters, documents and topics. Summary sentences are ranked by a regression SVM. The summarizer does not use any expensive NLP techniques such as parsing, tagging of names or even part of speech information. Still, the achieved accuracy is comparable to the best systems presented in recent academic competitions (i.e., Document Understanding Conference (DUC)). Because of a detailed feature analysis using Least Angle Regression (LARS), FastSum can rely on a minimal set of features leading to fast processing times: 1250 news documents in 60 seconds. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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