Review on Abstractive Text Summarization Techniques for Biomedical Domain

In this internet era amount of biomedical literature and data are increased exponentially. In order to keep up to date with knowledge of this field and other related area information also interpret the outcome of experiments in light of all available literature, researchers turn more and more to the use of automated literature mining. Biomedical or Biological domain is all about studying life and tremendous amount of biomedical textual information has produced and collected all over the world on daily basis. The task of analyzing huge amount of biomedical data and association of biological data is much difficult. To efficiently analyze the biomedical domain data text summarization approach is used. Automatic text summarization provides solution by generating summary automatically. Text summarization techniques classified into extractive and abstractive text summarization types. Existing techniques of extractive text summarization extract important sentences from original document and generate summary without any modification of actual data. This technique may not present conflicting information properly. Abstractive text summarization can solve this problem by representing the extracted sentences into another understandable semantic form. This paper discusses abstractive text summarization techniques and highlights the parametric evaluation of these techniques.