Improving Performance Text Summarization by Valizadeh Mohammadreza (8 results)

Taschenbuch. Condition: Neu. Improving the Performance of Text Summarization | Mohammadreza Valizadeh | Taschenbuch | 136 S. | Englisch | 2015 | SPS | EAN 9783639764659 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Condition: New.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book is concerned with the area of automatic summarization of multiple documents. We propose several methods that improve the accuracy of the summaries. We have explored document density in graph-based multi-document summarization. We have tested the assumption that the higher the density of a document, the higher the salience (score) of its sentences leading to better summaries.We have also done work on user-based models of multi-document summarization. We note that different users can generate rather different summaries on the basis of the same source data and query. We have exploited machine learning techniques to generate models oriented to specific users, but for the same query and source data. Also, we have explored the actor-object relationship (AOR) between sentences. This work lead again to marked improvements in the overall result, particularly when combined with the rather successful approach that involves ensemble summarizing system. 136 pp. Englisch.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Valizadeh MohammadrezaMohammadreza Valizadeh was born in Mehran, Iran 1978. He received the B.Sc degree in electronic and computer faculty from Isfahan University of Technology, Isfahan, Iran in 2000 and M.S. in artificial intelligen.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book is concerned with the area of automatic summarization of multiple documents. We propose several methods that improve the accuracy of the summaries. We have explored document density in graph-based multi-document summarization. We have tested the assumption that the higher the density of a document, the higher the salience (score) of its sentences leading to better summaries.We have also done work on user-based models of multi-document summarization. We note that different users can generate rather different summaries on the basis of the same source data and query. We have exploited machine learning techniques to generate models oriented to specific users, but for the same query and source data. Also, we have explored the actor-object relationship (AOR) between sentences. This work lead again to marked improvements in the overall result, particularly when combined with the rather successful approach that involves ensemble summarizing system.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 136 pp. Englisch.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book is concerned with the area of automatic summarization of multiple documents. We propose several methods that improve the accuracy of the summaries. We have explored document density in graph-based multi-document summarization. We have tested the assumption that the higher the density of a document, the higher the salience (score) of its sentences leading to better summaries.We have also done work on user-based models of multi-document summarization. We note that different users can generate rather different summaries on the basis of the same source data and query. We have exploited machine learning techniques to generate models oriented to specific users, but for the same query and source data. Also, we have explored the actor-object relationship (AOR) between sentences. This work lead again to marked improvements in the overall result, particularly when combined with the rather successful approach that involves ensemble summarizing system.

Condition: New. Print on Demand.

Condition: New. PRINT ON DEMAND.