AbstractThis paper proposes a novel similarity measure for automatic text summarization. The topic space model is built through the Latent Dirichlet Allocation. The word, sentence, document and corpus are represented as vectors in the same topic space. LMMR and LSD algorithm are introduced to create the summary. An experiment is illustrated on DUC data and the results prove the proposed measure and algorithm effective and well performed
Automatic text summarization generates a summary that contains sentences reflecting the essential an...
A major problem with automatically-produced summaries in general, and extracts in particular, is tha...
Many approaches have been introduced to enable Latent Dirichlet Allocation (LDA) models to be update...
AbstractThis paper proposes a novel similarity measure for automatic text summarization. The topic s...
With the advent and popularity of big data mining and huge text analysis in modern times, automated ...
In this paper, we present a novel approach that makes use of topic models based on Latent Dirichlet ...
In this paper, we present Latent Drichlet Allocation in automatic text summarization to improve accu...
We present in this paper experiments with several semantic similarity measures based on the unsuperv...
We study and propose in this article several novel solutions to the task of semantic similarity betw...
Text mining has a wide range of applications in education. In this paper, we review Latent Dirichlet...
Abstract: This paper deals with using latent semantic analysis in text summarization. We describe a ...
The technology of summarizing documents automatically is increasing rapidly and may give an answer f...
International audienceText summarization methods are much needed to tackle the ever-increasing volum...
We present in this paper the results of our investigation on semantic similarity measures at word- a...
In this study we propose an automatic single document text summarization technique using Latent Sema...
Automatic text summarization generates a summary that contains sentences reflecting the essential an...
A major problem with automatically-produced summaries in general, and extracts in particular, is tha...
Many approaches have been introduced to enable Latent Dirichlet Allocation (LDA) models to be update...
AbstractThis paper proposes a novel similarity measure for automatic text summarization. The topic s...
With the advent and popularity of big data mining and huge text analysis in modern times, automated ...
In this paper, we present a novel approach that makes use of topic models based on Latent Dirichlet ...
In this paper, we present Latent Drichlet Allocation in automatic text summarization to improve accu...
We present in this paper experiments with several semantic similarity measures based on the unsuperv...
We study and propose in this article several novel solutions to the task of semantic similarity betw...
Text mining has a wide range of applications in education. In this paper, we review Latent Dirichlet...
Abstract: This paper deals with using latent semantic analysis in text summarization. We describe a ...
The technology of summarizing documents automatically is increasing rapidly and may give an answer f...
International audienceText summarization methods are much needed to tackle the ever-increasing volum...
We present in this paper the results of our investigation on semantic similarity measures at word- a...
In this study we propose an automatic single document text summarization technique using Latent Sema...
Automatic text summarization generates a summary that contains sentences reflecting the essential an...
A major problem with automatically-produced summaries in general, and extracts in particular, is tha...
Many approaches have been introduced to enable Latent Dirichlet Allocation (LDA) models to be update...