Evaluation of Unsupervised Learning based Extractive Text Summarization Technique for Large Scale Review and Feedback Data

Background/Objectives: Supervised techniques uses human generated summary to select features and parameter for summarization. The main problem in this approach is reliability of summary based on human generated parameters and features. Many researches have shown the conflicts in summary generated. Due to diversity of large scale datasets, supervised techniques based summarization also fails to meet the requirements. Big data analytics for text dataset also recommends unsupervised techniques than supervised techniques. Unsupervised techniques based summarization systems finds representative sentences from large amount of text dataset. Methods/Statistical Analysis: Co-selection based evaluation measure is applied for evaluating the proposed research work. The value of recall, precision, f-measure and similarity measure are determined for concluding the research outcome for the respective objective. Findings: The algorithms like KMeans, MiniBatchKMeans, and Graph based summarization techniques are discussed with all technical details. The results achieved by applying Graph Based Text Summarization techniques with large scale review and feedback data found improvement over previously published results based on sentence scoring using TF and TF-IDF. Graph based sentence scoring method is much efficient than other unsupervised learning techniques applied for extractive text summarization. Application/Improvements: The execution of graph based algorithm with Spark's Graph X programming environment will secure execution time for this types of large scale review and feedback dataset which is considered under Big Data Problem.