Log In Sign Up

Tone Biased MMR Text Summarization

by   Mayank Chaudhari, et al.

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 naïve 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.


page 1

page 2

page 3

page 4


From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information

Text summarization is the research area aiming at creating a short and c...

KLearn: Background Knowledge Inference from Summarization Data

The goal of text summarization is to compress documents to the relevant ...

Controlled Text Reduction

Producing a reduced version of a source text, as in generic or focused s...

Leveraging BERT for Extractive Text Summarization on Lectures

In the last two decades, automatic extractive text summarization on lect...

Unsupervised Extractive Summarization by Human Memory Simulation

Summarization systems face the core challenge of identifying and selecti...

Real-Time Web Scale Event Summarization Using Sequential Decision Making

We present a system based on sequential decision making for the online s...

Summary Explorer: Visualizing the State of the Art in Text Summarization

This paper introduces Summary Explorer, a new tool to support the manual...