| Challenge: | Multimodal summarization materials lacking a holistic organization by integrating resources from various modalities. |
| Approach: | They propose a multimodal article and video summarization dataset that integrates resources from different modalities. |
| Outcome: | The proposed dataset validates the important assistance role of external information for multimodal summarization. |
Similar Papers
Large Scale Multi-Lingual Multi-Modal Summarization Dataset (2023.eacl-main)
Copied to clipboard
| Challenge: | a large dataset of document-image pairs and annotated multi-modal summarization data is needed for multi-lingual modeling . encoder-decoder models represent information comprising multiple modalities. |
| Approach: | They propose to use a multi-lingual summarization dataset to analyze multi-modal summarizing using multi-linguistic annotated data. |
| Outcome: | The proposed dataset is the largest multi-lingual multi-modal summarization dataset for 13 languages and consists of cross-lingual summarizing data for 2 languages. |
MSMO: Multimodal Summarization with Multimodal Output (D18-1)
Copied to clipboard
| Challenge: | Existing studies show that multimodal summarization can improve user satisfaction for informativeness of summaries by using information in visual modality. |
| Approach: | They propose a task to generate text and select the most relevant image from the multimodal input and a novel multimodal automatic evaluation method to evaluate multimodal outputs. |
| Outcome: | The proposed method improves user satisfaction by 12.4% compared to the current system . |
MDS: A Fine-Grained Dataset for Multi-Modal Dialogue Summarization (2024.lrec-main)
Copied to clipboard
| Challenge: | Summarizing the dialogue into a short message has drawn much attention due to the explosion of various dialogue scenes. |
| Approach: | They develop a multi-modal dialogue summarization dataset to enhance the variety of data available for this research area. |
| Outcome: | The proposed dataset provides a demanding testbed for multi-modal dialogue summarization. |
Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model (P19-1)
Copied to clipboard
| Challenge: | Multi-document summarization (MDS) of news articles has been limited to datasets of a couple of hundred examples. |
| Approach: | They propose a model which integrates a traditional extractive summarization model with a standard SDS model and achieves competitive results on MDS datasets. |
| Outcome: | The proposed model achieves competitive results on large-scale datasets. |
Hierarchical3D Adapters for Long Video-to-text Summarization (2023.findings-eacl)
Copied to clipboard
| Challenge: | a recent study shows that multimodal summarization is not efficient for long inputs and outputs. |
| Approach: | They extend a TV episode transcript summarization dataset and create a multimodal variant by collecting full-length videos. |
| Outcome: | The proposed model can be tuned to perform multimodal summarization tasks efficiently using adapter modules augmented with a hierarchical structure while tuning only 3.8% of model parameters. |
A Modular Approach for Multimodal Summarization of TV Shows (2024.acl-long)
Copied to clipboard
| Challenge: | In this paper, we address the task of summarizing television shows, which touches key areas in AI research. |
| Approach: | They propose a modular approach where separate components perform specialized sub-tasks . they propose atomic facts to measure precision and recall of generated summaries . |
| Outcome: | The proposed method produces higher quality summaries than comparison models on a recently released dataset. |
Pay More Attention to Images: Numerous Images-Oriented Multimodal Summarization (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing multimodal summarization approaches struggle with scenarios involving multiple images as input. |
| Approach: | They propose a task to generate multimodal summaries by integrating multiple images as input . they propose 'multimodal information evaluation' method that measures differences between generated summary and input based on multimodal input - and compares various methods . |
| Outcome: | The proposed method correlates more closely with human judgments than five widely used metrics . |
Multimodality for NLP-Centered Applications: Resources, Advances and Frontiers (2022.lrec-1)
Copied to clipboard
| Challenge: | resurgence of multimodal datasets has attracted significant research interest, but there is no comprehensive survey for this task. |
| Approach: | They present a survey of a multimodal dataset with different modalities according to the applications. |
| Outcome: | The proposed datasets are available online and discuss the new frontier and motivate future researches. |
Multimodal Large Language Models for Text-rich Image Understanding: A Comprehensive Review (2025.findings-acl)
Copied to clipboard
Pei Fu, Tongkun Guan, Zining Wang, Zhentao Guo, Chen Duan, Hao Sun, Boming Chen, Qianyi Jiang, Jiayao Ma, Kai Zhou, Junfeng Luo
| Challenge: | Recent advances in vision-language models have unified perception and understanding tasks within Visual Question Answering paradigms. |
| Approach: | They propose to outline timeline, architecture, and pipeline of nearly all TIU MLLMs and review their performance on mainstream benchmarks. |
| Outcome: | The proposed models perform well on mainstream benchmarks and are compared with other models. |
VMSMO: Learning to Generate Multimodal Summary for Video-based News Articles (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies show that multimodal news can significantly improve users' sense of satisfaction for informativeness. |
| Approach: | They propose a task of Video-based Multimodal Summarization with Multimodal Output to solve this problem. |
| Outcome: | The proposed method can generate multimodal summaries with a single input . it can model the temporal dependency of video with semantic meaning of article . |