# Leveraging Entity Information for Cross-Modality Correlation Learning: The Entity-Guided Multimodal Summarization

arXiv: https://arxiv.org/abs/2408.03149 · HTML: https://mlsanity.com/paper/2408.03149/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 6 Aug 2024.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering](https://mlsanity.com/paper/2007.01282/) (2020)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification](https://mlsanity.com/paper/2108.02035/) (2021)
- [BRIO: Bringing Order to Abstractive Summarization](https://mlsanity.com/paper/2203.16804/) (2022)
- [Integrating Semantic Knowledge to Tackle Zero-shot Text Classification](https://mlsanity.com/paper/1903.12626/) (2019)
- [UniMS: A Unified Framework for Multimodal Summarization with Knowledge Distillation](https://mlsanity.com/paper/2109.05812/) (2021)
- [Hierarchical Cross-Modality Semantic Correlation Learning Model for Multimodal Summarization](https://mlsanity.com/paper/2112.12072/) (2021)

## Built on by (most cited first, 4 of 4)

- [Layer-Order Inversion: Rethinking Latent Multi-Hop Reasoning in Large Language Models](https://mlsanity.com/paper/2601.03542/) (2026)
- [WDMIR: Wavelet-Driven Multimodal Intent Recognition](https://mlsanity.com/paper/2506.10011/) (2025)
- [Fine-Grained Zero-Shot Composed Image Retrieval with Complementary Visual-Semantic Integration](https://mlsanity.com/paper/2601.14060/) (2026)
- [Less Is More? Selective Visual Attention to High-Importance Regions for Multimodal Radiology Summarization](https://mlsanity.com/paper/2603.29901/) (2026)

## Abstract (arXiv)

The rapid increase in multimedia data has spurred advancements in Multimodal Summarization with Multimodal Output (MSMO), which aims to produce a multimodal summary that integrates both text and relevant images. The inherent heterogeneity of content within multimodal inputs and outputs presents a significant challenge to the execution of MSMO. Traditional approaches typically adopt a holistic perspective on coarse image-text data or individual visual objects, overlooking the essential connections between objects and the entities they represent. To integrate the fine-grained entity knowledge, we propose an Entity-Guided Multimodal Summarization model (EGMS). Our model, building on BART, utilizes dual multimodal encoders with shared weights to process text-image and entity-image information concurrently. A gating mechanism then combines visual data for enhanced textual summary generation, while image selection is refined through knowledge distillation from a pre-trained vision-language model. Extensive experiments on public MSMO dataset validate the superiority of the EGMS method, which also prove the necessity to incorporate entity information into MSMO problem.