Our Experience with Text Summarization
With the present availability of abundant data in digital space, which is mostly non-structured text data, there is a need to develop automatic text summarization tools to get insights from data easily. Text Summarization as a concept is quite old and yet a difficult task. It aims at generating concise and precise summary of voluminous texts while focusing on the sections that convey useful information, and without losing the overall meaning.
There are two kinds of Text summarization techniques, Extractive and Abstractive summarization. This paper is about the different technologies and approaches that are combined in order to generate effective yet meaningful Extractive summarization. We are showcasing a summary of financial research reports as a use case.
We (Humans) are generally good at summarization as it is a cognitive activity.