Intelligent narrative summaries: from indicative to informative summarization

Perceiving data can be challenging as the analysis goal is subjective. Storytelling approaches try to address this challenge by focusing on the research problem of understanding the data in general and, more particularly in curation, summarization, and presentation of the big data, i.e., a massive number of small data islands from personal, shared, and business data. In this context, summarization can facilitate understanding and surfacing insights that are embedded within the data. In this paper, we propose a general hierarchical personalized summarization framework, called Narrative Summaries (NARS), to improve the drawbacks of traditional summarization methods in various aspects. We proposed two variants of NARS: a semi-structured summarization approach (SNARS) and a fully-structured summarization approach (FNARS). The goal is to enable intelligent narrative construction of summaries based on the important features extracted from users' engagement. To achieve this goal, we propose a hierarchical structure to prevent users from being overwhelmed with less important information at first glance and to facilitate the selection process. Afterward, instead of providing a short static summary, we present an intelligent and interactive summarization approach to enable users to navigate through the hierarchy to gain more elaborated information upon request. Our approach aims to: (i) engage users in the summarization process, to guarantee the interactive speed even for extensive text collections, and (ii) eliminate the need for reference summaries, which is one of the most challenging issues in the summarization problem. The results demonstrate that our approach fabricates a summary that helps the users understand the topic better and faster compared to the existing state-of-the-art methods.