A Survey of Unstructured Text Summarization Techniques

Abstract—Due to the explosive amounts of text data being created and organizations increased desire to leverage their data corpora, especially with the availability of Big Data platforms, there is not usually enough time to read and understand each document and make decisions based on document contents. Hence, there is a great demand for summarizing text documents to provide a representative substitute for the original documents. By improving summarizing techniques, precision of document retrieval through search queries against summarized documents is expected to improve in comparison to querying against the full spectrum of original documents. Several generic text summarization algorithms have been developed, each with its own advantages and disadvantages. For example, some algorithms are particularly good for summarizing short documents but not for long ones. Others perform well in identifying and summarizing single-topic documents but their precision degrades sharply with multi-topic documents. In this article we present a survey of the literature in text summarization. We also surveyed some of the most common evaluation methods for the quality of automated text summarization techniques. Last, we identified some of the challenging problems that are still open, in particular the need for a universal approach that yields good results for mixed types of documents. Keywords—text summarization; unstructured data; text mining; unstructured data analytics I