Abstract
In this paper, we focus on the task of automatic text summarization. Lot of work has already been carried out on automatic text summarization though most of the work done in this field is on extracted summaries. We have developed a tool that summarizes the given text. We have used several NLP features and machine learning techniques for text summarizing. We have also showed how WordNet can be used to obtain abstractive summarization. We are using an approach that first extracts sentences from the given text by using ranking algorithm, by means of which we rank the sentence on the basis of many features comprising of some classical features as well as some novel ones. Then, after extracting candidate sentences, we investigate some of the words and phrases and transform them into their respective simple substitutes so as to make the final summary a hybrid summarization technique.
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Mudasir Mohd et al. (2016). Sumdoc: A Unified Approach for Automatic Text Summarization. In: Pant, M., Deep, K., Bansal, J., Nagar, A., Das, K. (eds) Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 436. Springer, Singapore. https://doi.org/10.1007/978-981-10-0448-3_27
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DOI: https://doi.org/10.1007/978-981-10-0448-3_27
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