Summary of Unified Multimodal Interleaved Document Representation For Retrieval, by Jaewoo Lee and Joonho Ko and Jinheon Baek and Soyeong Jeong and Sung Ju Hwang
Unified Multimodal Interleaved Document Representation for Retrieval
by Jaewoo Lee, Joonho Ko, Jinheon Baek, Soyeong Jeong, Sung Ju Hwang
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel method for Information Retrieval (IR) that addresses two limitations in existing approaches. Firstly, it considers the fact that documents often contain multiple modalities such as images and tables, which are typically overlooked by current IR methods. Secondly, it mitigates the information loss caused by segmenting long documents into passages, instead representing and retrieving the entire document. The proposed method leverages recent vision-language models to holistically embed documents with multimodal content, followed by a reranking strategy to identify relevant passages within the document. Through extensive experiments on various IR scenarios, including textual and multimodal queries, the approach outperforms relevant baselines, demonstrating its effectiveness in capturing the overall document context and interactions between paragraphs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making search engines better at finding what you’re looking for when you search online. Currently, search engines only look at words on a webpage, but webpages can also have pictures, tables, and other kinds of content that are important too. The researchers created a new way to search that takes all this extra information into account, so it’s better at finding what you want. They tested their method with many different types of searches and found that it worked much better than the old ways of searching. This could make searching online more accurate and helpful for everyone. |
