Text Summarization Techniques: A Brief Survey <s> KNOWLEDGE BASES AND AUTOMATIC SUMMARIZATION <s> As huge amounts of knowledge are created rapidly, effective information access becomes an important issue. Especially for critical domains, such as medical and financial areas, efficient retrieval of concise and relevant information is highly desired. In this paper we propose a new user query based text summarization technique that makes use of unified medical language system, an ontology knowledge source from National Library of Medicine. We compare our method with keyword-only approach, and our ontology-based method performs clearly better. Our method also shows potential to be used in other information retrieval areas <s> BIB001 </s> Text Summarization Techniques: A Brief Survey <s> KNOWLEDGE BASES AND AUTOMATIC SUMMARIZATION <s> Sentence-based multi-document summarization is the task of generating a succinct summary of a document collection, which consists of the most salient document sentences. In recent years, the increasing availability of semantics-based models (e.g., ontologies and taxonomies) has prompted researchers to investigate their usefulness for improving summarizer performance. However, semantics-based document analysis is often applied as a preprocessing step, rather than integrating the discovered knowledge into the summarization process. This paper proposes a novel summarizer, namely Yago-based Summarizer, that relies on an ontology-based evaluation and selection of the document sentences. To capture the actual meaning and context of the document sentences and generate sound document summaries, an established entity recognition and disambiguation step based on the Yago ontology is integrated into the summarization process. The experimental results, which were achieved on the DUC'04 benchmark collections, demonstrate the effectiveness of the proposed approach compared to a large number of competitors as well as the qualitative soundness of the generated summaries. <s> BIB002 </s> Text Summarization Techniques: A Brief Survey <s> KNOWLEDGE BASES AND AUTOMATIC SUMMARIZATION <s> Abstract Automatic text summarization has been an active field of research for many years. Several approaches have been proposed, ranging from simple position and word-frequency methods, to learning and graph based algorithms. The advent of human-generated knowledge bases like Wikipedia offer a further possibility in text summarization – they can be used to understand the input text in terms of salient concepts from the knowledge base. In this paper, we study a novel approach that leverages Wikipedia in conjunction with graph-based ranking. Our approach is to first construct a bipartite sentence–concept graph, and then rank the input sentences using iterative updates on this graph. We consider several models for the bipartite graph, and derive convergence properties under each model. Then, we take up personalized and query-focused summarization, where the sentence ranks additionally depend on user interests and queries, respectively. Finally, we present a Wikipedia-based multi-document summarization algorithm. An important feature of the proposed algorithms is that they enable real-time incremental summarization – users can first view an initial summary, and then request additional content if interested. We evaluate the performance of our proposed summarizer using the ROUGE metric, and the results show that leveraging Wikipedia can significantly improve summary quality. We also present results from a user study, which suggests that using incremental summarization can help in better understanding news articles. <s> BIB003
The goal of automatic text summarization is to create summaries that are similar to human-created summaries. However, in many cases, the soundness and readability of created summaries are not satisfactory, because the summaries do not cover all the semantically relevant aspects of data in an effective way. This is because many of the existing text summarization techniques do not consider the semantics of words. A step towards building more accurate summarization systems is to combine summarization techniques with knowledge bases (semantic-based or ontology-based summarizers). The advent of human-generated knowledge bases and various ontologies in many different domains (e.g. Wikipedia, YAGO, DBpedia, etc) has opened further possibilities in text summarization , and reached increasing attention recently. For example, Henning et al.  present an approach to sentence extraction that maps sentences to concepts of an ontology. By considering the ontology features, they can improve the semantic representation of sentences which is beneficial in selection of sentences for summaries. They experimentally showed that ontology-based extraction of sentences outperforms baseline summarizers. Chen et al. BIB001 introduce a user query-based text summarizer that uses the UMLS medical ontology to make a summary for medical text. Baralis et al. BIB002 propose a Yago-based summarizer that leverages YAGO ontology  to identify key concepts in the documents. The concepts are evaluated and then used to select the most representative document sentences. Sankarasubramaniam et al. BIB003 introduce an approach that employs Wikipedia in conjunction with a graph-based ranking technique. First, they create a bipartite sentence-concept graph, and then use an iterative ranking algorithm for selecting summary sentences.