# Multi-Modal Summary Generation using Multi-Objective Optimization

arXiv: https://arxiv.org/abs/2005.09252 · HTML: https://mlsanity.com/paper/2005.09252/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2020.

## Built on by (most cited first, 8 of 8)

- [A Survey on Multi-modal Summarization](https://mlsanity.com/paper/2109.05199/) (2021)
- [A Survey on Medical Document Summarization](https://mlsanity.com/paper/2212.01669/) (2022)
- [Summary-Oriented Vision Modeling for Multimodal Abstractive Summarization](https://mlsanity.com/paper/2212.07672/) (2022)
- [Large Scale Multi-Lingual Multi-Modal Summarization Dataset](https://mlsanity.com/paper/2302.06560/) (2023)
- [Exploiting Pseudo Image Captions for Multimodal Summarization](https://mlsanity.com/paper/2305.05496/) (2023)
- [A Multimodal In-Context Tuning Approach for E-Commerce Product Description Generation](https://mlsanity.com/paper/2402.13587/) (2024)
- [Semantic Extractor-Paraphraser based Abstractive Summarization](https://mlsanity.com/paper/2105.01296/) (2021)
- [WIDAR -- Weighted Input Document Augmented ROUGE](https://mlsanity.com/paper/2201.09282/) (2022)

## Abstract (arXiv)

Significant development of communication technology over the past few years has motivated research in multi-modal summarization techniques. A majority of the previous works on multi-modal summarization focus on text and images. In this paper, we propose a novel extractive multi-objective optimization based model to produce a multi-modal summary containing text, images, and videos. Important objectives such as intra-modality salience, cross-modal redundancy and cross-modal similarity are optimized simultaneously in a multi-objective optimization framework to produce effective multi-modal output. The proposed model has been evaluated separately for different modalities, and has been found to perform better than state-of-the-art approaches.