Update README.md

README.md

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 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/66337cb5bd8ef15a47e72ce0/hhAX190AZageb5Bqqr_lp.png)
 
 
-## Abstract
+## 1. Abstract
 Multi-modal document retrieval is designed to identify and retrieve various forms of multi-modal content, such as figures, tables, charts, and layout information from extensive documents.
 Despite its significance, there is a notable lack of a robust benchmark to effectively evaluate the performance of systems in multi-modal document retrieval.
 To address this gap, this work introduces a new benchmark, named as **MMDocIR**, encompassing two distinct tasks: **page-level** and **layout-level** retrieval. 
@@ -27,18 +27,29 @@ Through rigorous experiments, we reveal that
 These findings underscores the potential advantages of integrating visual elements for multi-modal document retrieval.
 
 
-## Evaluation Set
-### Document Analysis
+## 2. Task Setting
+### Page-level Retrieval
+The page-level retrieval task is designed to identify the most relevant pages within a document in response to a user query.
+
+### Layout-level Retrieval
+The layout-level retrieval aims to retrieve most relevant layouts. 
+The layouts are defined as the fine-grained elements such as paragraphs, equations, figures, tables, and charts. 
+This task allows for a more nuanced content retrieval, honing in on specific information that directly answers user queries.
+
+
+
+## 3. Evaluation Set
+### 3.1 Document Analysis
 
 **MMDocIR** evluation set includes 313 long documents averaging 65.1 pages, categorized into ten main domains: research reports, administration&industry, tutorials&workshops, academic papers, brochures, financial reports, guidebooks, government documents, laws, and news articles. 
 Different domains feature distinct distributions of multi-modal information. For instance, research reports, tutorials, workshops, and brochures predominantly contain images, whereas financial and industry documents are table-rich. In contrast, government and legal documents primarily comprise text. Overall, the modality distribution is: Text (60.4%), Image (18.8%), Table (16.7%), and other modalities (4.1%).
 
-### Question and Annotation Analysis
+### 3.2 Question and Annotation Analysis
 **MMDocIR** evluation set encompasses 1,658 questions, 2,107 page labels, and 2,638 layout labels. The modalities required to answer these questions distribute across four categories: Text (44.7%), Image (21.7%), Table (37.4%), and Layout/Meta (11.5%). The ``Layout/Meta'' category encompasses questions related to layout information and meta-data statistics. 
 Notably, the dataset poses several challenges: 254 questions necessitate cross-modal understanding, 313 questions demand evidence across multiple pages, and 637 questions require reasoning based on multiple layouts. These complexities highlight the need for advanced multi-modal reasoning and contextual understanding.
 
 
-## Train Set
+## 4. Train Set