Summarization Techniques for Biomedical Domain

ive summarization classified into two categories: structured base and semantic base. In Abstractive Semantic based method, semantic representation of document(s) used to feed into natural language generation (NLG) system. This method focus on identifying noun phrases and verb phrases by processing linguistic data. Different techniques utilized this approach are discussed here [10][14][16]: Multimodal semantic model catches the ideas and relationship among source information that important ideas evaluated based on some measures and finally the selected concepts expressed as sentences to form summary. In information item based method the contents of summary generated from abstract representation of source documents. The abstract representation called information item, in which the smallest element of coherent information in a text. In Semantic graph based method summary of document forms by creation a rich semantic graph (RSH) of the original document, reduced the generated semantic graph, and then generating the final abstractive summary. Semantic text representation method analyzed input text using semantics of words rather than syntax structure of text. 3. Various Techniques for Text Summarization Most of work done in text summarization has focused in this section, we discuss different approaches and some works on abstractive text summarization. A. Semantic Graph Reduction Approach This approach [1] outlines an input document by creating semantic graph called as rich semantic graph (RSG). The semantic graph further reduced and generates final abstractive summary from reduced semantic graph. System takes input as a solitary document in English language and output generated as reduced summary report. Fig. 2.0: Architecture of Semantic Graph Reduction Approach This approach comprises of three task. The first task is RSG creation. The main aim of the RSG creation to represent the input document semantically. In that verb and nouns of input document represented as graph nodes and edges represented as semantic relations between them. In this way it builds the graph for each sentence and afterward it interconnects rich semantic sub-graphs. At the end the sub-graphs, all the subgraph merged together to represent the whole document semantically. The second phase called RSG reduction. In this phase a set of rules applied on RSG to reduce it by merging, deleting the graph nodes. Third phase generates abstractive summary from reduced RSG. This approach succeeds to reduce the source document up to half of the original document. Limitation of this approaches that no multiple documents taken as input to generate abstractive summary. B. Word Graph based Approach This approach uses word graph to represent source document. This approach includes two phases [2]. First phase sentence reduction and second sentence combination. The sentence reduction phase based on discourse rules to remove Source document Rich Semantic Graph Creation Rich Semantic Graph Reduction Summarized Text Generation Source Documents Rich Semantic Graph Creation Rich Semantic Graph Reduction Summarized Text Generation Volume-03, Issue-05, May-2018 RESEARCH REVIEW International Journal of Multidisciplinary © RRIJM 2015, All Rights Reserved 23 | Page redundant clauses at the beginning of a sentence, and syntactic constraints to complete the end of the reduced sentence. Word graph used for sentence combinations and to represent word relations between texts [12]. New sentences are generated from several sentences which are generated by using word graph. In word graph nodes used to store the information about words and their part of speech tagger and edges used to represent adjacency relations between word pairs. This approach generate syntactically correct sentence but does not care about