by MEGHANA VISWANATH (Under the Direction of Krzysztof J. Kochut) ABSTRACT This thesis presents an ontology-based approach to automatic extractive summarization of text. Most of the extractive summarization systems so far have used statistical importance measures to determine importance of sentences. We use a knowledge-based approach which makes use of ontological knowledge to determine sentenc...

We propose a graph-based method for extractive single-document summarization which considers importance, non-redundancy and local coherence simultaneously. We represent input documents by means of a bipartite graph consisting of sentence and entity nodes. We rank sentences on the basis of importance by applying a graph-based ranking algorithm to this graph and ensure non-redundancy and local co...

The need for text summarization is crucial as we enter the era of information overload. However, the current implementations are specific to a domain or a genre of the source document. In this paper, we discuss an algorithm for text summarization which is independent of domain and document source. This algorithm creates text summaries by analyzing the logical structure of the sentences. Sentenc...

The Video summarization normally includes shot boundary detection, key frame extraction from each shot and generation of summary. The proposed video summarization approach eliminates shot boundary detection by employing segmentation based key frame extraction scheme and the segmentation is achieved by fuzzy C Means (FCM) clustering. The video summarization algorithms basically perform structura...

Twitter is a fast communication channel for gathering and spreading breaking news, it generates large volume of tweets most events. Automatically creating summary an event necessary important. In this study, we explored two extractive approaches summarizing events on Twitter. The first one exploits the semantic types related terms, ranks based score computed from these terms. second utilizes gr...

Timeline summarization (TLS) is defined as a task for summarizing events in chronological order, which gives readers comprehensive understanding of an evolutionary story. Previous studies on the timeline ignored information interaction between sentences and dates, adopted pre-defined unlearnable representations them, significantly degrade performance. They also considered date selection event d...