A Rhetorical Relations-Based Framework for Tailored Multimedia Document Summarization

TL;DR

This paper presents a novel framework for multimedia document summarization that leverages rhetorical structure and graph-based analysis to produce coherent, personalized, and context-aware summaries across diverse content formats.

Contribution

It introduces a new framework combining rhetorical analysis, graph modeling, and weighting algorithms for tailored multimedia summarization, enhancing coherence and personalization.

Findings

Effective extraction of key information from multimedia content

Framework accommodates user preferences and constraints

Demonstrated potential for broad multimedia summarization applications

Abstract

In the rapidly evolving landscape of digital content, the task of summarizing multimedia documents, which encompass textual, visual, and auditory elements, presents intricate challenges. These challenges include extracting pertinent information from diverse formats, maintaining the structural integrity and semantic coherence of the original content, and generating concise yet informative summaries. This paper introduces a novel framework for multimedia document summarization that capitalizes on the inherent structure of the document to craft coherent and succinct summaries. Central to this framework is the incorporation of a rhetorical structure for structural analysis, augmented by a graph-based representation to facilitate the extraction of pivotal information. Weighting algorithms are employed to assign significance values to document units, thereby enabling effective ranking and selection of relevant content. Furthermore, the framework is designed to accommodate user preferences and time constraints, ensuring the production of personalized and contextually relevant summaries. The summarization process is elaborately delineated, encompassing document specification, graph construction, unit weighting, and summary extraction, supported by illustrative examples and algorithmic elucidation. This proposed framework represents a significant advancement in automatic summarization, with broad potential applications across multimedia document processing, promising transformative impacts in the field.