Tone Biased MMR Text Summarization

TL;DR

This paper introduces a method to generate tone-biased multi-document summaries by modifying Maximal Marginal Relevance to favor specific words, aligning the summary's tone with desired emotional or stylistic cues.

Contribution

It proposes a naive model that biases MMR-based summarization towards specific words to control the tone of generated summaries, addressing a gap in tone-aware summarization.

Findings

Successfully biases summary tone towards specified words

Demonstrates control over summary emotional and stylistic tone

Enhances relevance of summaries with tone alignment

Abstract

Text summarization is an interesting area for researchers to develop new techniques to provide human like summaries for vast amounts of information. Summarization techniques tend to focus on providing accurate representation of content, and often the tone of the content is ignored. Tone of the content sets a baseline for how a reader perceives the content. As such being able to generate summary with tone that is appropriate for the reader is important. In our work we implement Maximal Marginal Relevance [MMR] based multi-document text summarization and propose a naive model to change tone of the summarization by setting a bias to specific set of words and restricting other words in the summarization output. This bias towards a specified set of words produces a summary whose tone is same as tone of specified words.