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Topic modeling algorithms are statistical methods that aim to discover the topics running through the text documents. Using topic models in machine learning and text mining is popular due to its applicability in inferring the latent topic structure of a corpus. In this paper, we represent an enriching document approach, using state-ofthe-art topic models and data fusion methods, to enrich documents of a collection with the aim of improving the quality of text clustering and cluster labeling. We propose a bi-vector space model in which every document of the corpus is represented by two vectors: one is generated based on the fusion-based topic modeling approach, and one simply is the traditional vector model. Our experiments on various datasets show that using a combination of topic modeling and fusion methods to create documents’ vectors can significantly improve the quality of the results in clustering the documents.

This paper explores a multi-strategy technique that aims at enriching text documents for improving clustering quality. We use a combination of entity linking and document summarization in order to determine the identity of the most salient entities mentioned in texts. To effectively enrich documents without introducing noise, we limit ourselves to the text fragments mentioning the salient entities, in turn, belonging to a knowledge base like Wikipedia, while the actual enrichment of text fragments is carried out using WordNet. To feed clustering algorithms, we investigate different document representations obtained using several combinations of document enrichment and feature extraction. This allows us to exploit ensemble clustering, by combining multiple clustering results obtained using different document representations. Our experiments indicate that our novel enriching strategies, combined with ensemble clustering, can improve the quality of classical text clustering when applied to text corpora like The British Broadcasting Corporation (BBC) NEWS.

Automatic text summarization is the process of reducing the size of a text document, to create a summary that retains the most important points of the original document. It can thus be applied to summarize the original document by decreasing the importance or removing part of the content. The contribution of this paper in this field is twofold. First we show that text summarization can improve the performance of classical text clustering algorithms, in particular by reducing noise coming from long documents that can negatively affect clustering results. Moreover, the clustering quality can be used to quantitatively evaluate different summarization methods. In this regards, we propose a new graph-based summarization technique for keyphrase extraction, and use the Classic4 and BBC NEWS datasets to evaluate the improvement in clustering quality obtained using text summarization.