A Survey of Unstructured Text Summarization Techniques <s> A. Query-relevant Summarization <s> In this paper, we propose two generic text summarization methods that create text summaries by ranking and extracting sentences from the original documents. The first method uses standard IR methods to rank sentence relevances, while the second method uses the latent semantic analysis technique to identify semantically important sentences, for summary creations. Both methods strive to select sentences that are highly ranked and different from each other. This is an attempt to create a summary with a wider coverage of the document's main content and less redundancy. Performance evaluations on the two summarization methods are conducted by comparing their summarization outputs with the manual summaries generated by three independent human evaluators. The evaluations also study the influence of different VSM weighting schemes on the text summarization performances. Finally, the causes of the large disparities in the evaluators' manual summarization results are investigated, and discussions on human text summarization patterns are presented. <s> BIB001 </s> A Survey of Unstructured Text Summarization Techniques <s> A. Query-relevant Summarization <s> In this paper, a new summarization method, which uses non-negative matrix factorization (NMF) and K-means clustering, is introduced to extract meaningful sentences from multi-documents. The proposed method can improve the quality of document summaries because the inherent semantics of the documents are well reflected by using the semantic features calculated by NMF and the sentences most relevant to the given topic are extracted efficiently by using the semantic variables derived by NMF. Besides, it uses K-means clustering to remove noises so that it can avoid the biased inherent semantics of the documents to be reflected in summaries. We perform detail experiments with the well-known DUC test dataset. The experimental results demonstrate that the proposed method has better performance than other methods using the LSA, the Kmeans, and the NMF. <s> BIB002 </s> A Survey of Unstructured Text Summarization Techniques <s> A. Query-relevant Summarization <s> Query-oriented summarization aims at extracting an informative summary from a document collection for a given query. It is very useful to help users grasp the main information related to a query. Existing work can be mainly classified into two categories: supervised method and unsupervised method. The former requires training examples, which makes the method limited to predefined domains. While the latter usually utilizes clustering algorithms to find ‘centered’ sentences as the summary. However, the method does not consider the query information, thus the summarization is general about the document collection itself. Moreover, most of existing work assumes that documents related to the query only talks about one topic. Unfortunately, statistics show that a large portion of summarization tasks talk about multiple topics. In this paper, we try to break limitations of the existing methods and study a new setup of the problem of multi-topic based query-oriented summarization. We propose using a probabilistic approach to solve this problem. More specifically, we propose two strategies to incorporate the query information into a probabilistic model. Experimental results on two different genres of data show that our proposed approach can effectively extract a multi-topic summary from a document collection and the summarization performance is better than baseline methods. The approach is quite general and can be applied to many other mining tasks, for example product opinion analysis and question answering. <s> BIB003
A query-relevant summary presents the document's contents that are closely related to an initial search query. This can be achieved by extending conventional information retrieval technologies. Depending on the user's supplied query, the text documents are searched for matches with that query, and a summary is created on the fly, which contains the sentences that have the query matches. The selection of sentences based on their ranking, with respect to a query, using latent semantic analysis (LSA) was proposed by Gong and Liu BIB001 . Park et al. proposed a new approach using a combination of Non-negative Matrix Factorization and K-means clustering to identify sentences based on a query. Their approach produced better performance than LSA BIB002 . Tang et al. retrieve relevant documents to a query, use a unified probabilistic approach to discover queryoriented topics and apply four scoring methods to calculate the importance of each sentence. Sentences with the highest score make the summary of each document BIB003 .