QUERY-BASED SUMMARIZATION METHODS FOR CONVERSATIONAL AGENTS: AN OVERVIEW

Abstract

Summarization is a topic that will be of a great important in the coming age since intelligent assistants especially the ones in the form of conversational agents will have to sift through the abundance of raw unstructured text data to provide relevant information. The data will be in the form of Social media posts, content websites and other user generated text content from which the user shall require tailored information from and about the data. The paper hence explores various methods for summarization and focuses particularly on extracting the gist from the perspective of a given keyword i.e. query based summarization from raw unstructured text data sources available at scale. Along with that, the need for a proper framework to mine relevant knowledge from the said data is acknowledged and the challenges that a conversational agent would hence face are identified. Various approaches that contribute to building a framework and solve the identified challenges are explored as well. It is hoped that the approaches discussed in the paper will be of use to researchers building algorithms in areas of knowledge mining and understanding, such as summarization, that deal with the challenges that are expected to arise.