| --- |
| license: mit |
| task_categories: |
| - text-generation |
| language: |
| - en |
| pretty_name: SurGE |
| size_categories: |
| - 100M<n<1B |
| --- |
| # SurGE |
|
|
| Welcome to the official Hugging Face repository for SurGE, a benchmark and dataset for end-to-end scientific survey generation in the computer science domain. |
|
|
| SurGE provides a comprehensive resource for evaluating automated survey generation systems through both a large-scale dataset and a fully automated evaluation framework. |
|
|
| More information at: https://github.com/oneal2000/SurGE |
|
|
| ## Overview |
|
|
| SurGE is designed to push the boundaries of automated survey generation by tackling the complex task of creating coherent, in-depth survey articles from a vast academic literature collection. Unlike traditional IR tasks focused solely on document retrieval, SurGE requires systems to: |
|
|
| - **Retrieve:** Identify relevant academic articles from a corpus of over 1 million papers. |
| - **Organize:** Construct a structured and hierarchical survey outline. |
| - **Synthesize:** Generate a coherent narrative with proper citations, reflecting expert-authored surveys. |
|
|
| The benchmark includes 205 carefully curated ground truth surveys, each accompanied by detailed metadata and a corresponding hierarchical structure, along with an extensive literature knowledge base sourced primarily from arXiv. |
|
|
| ## Data Release |
|
|
| This repository contains all necessary components for working with the SurGE dataset: |
|
|
| #### Dataset Files & Formats: |
|
|
| - **Ground Truth Surveys:** Each survey includes metadata fields such as title, authors, publication year, abstract, hierarchical structure, and citation lists. |
| - **Literature Knowledge Base:** A corpus of 1,086,992 academic papers with key fields (e.g., title, authors, abstract, publication date, and category). |
| - **Auxiliary Mappings:** Topic-to-publication mappings to support systematic survey generation. |
|
|
| The complete dataset can be downloaded from [this Google Drive folder](https://drive.google.com/drive/folders/1ZZPeZvjexFcCmgFqxftKeCPn1vYeBR0Q?usp=drive_link). |
|
|
| Then you will get the folder `data` . |
|
|
| #### Ground Truth Survey |
|
|
| A **ground truth survey** contains the full content of a survey and its citation information. However, due to space constraints, we cannot display it in its entirety here. |
|
|
| All ground truth surveys are available in `data/surveys.json` |
|
|
| A **survey** consists of the following fields: |
|
|
| | Field | Description | |
| | ------------ | --------------------------------------------------------- | |
| | authors | List of contributing researchers. | |
| | survey_title | The title of the survey paper. | |
| | year | The publication year of the survey. | |
| | date | The exact timestamp of publication. | |
| | category | Subject classification following the arXiv taxonomy. | |
| | abstract | The abstract of the survey paper. | |
| | structure | Hierarchical representation of the survey’s organization. | |
| | survey_id | A unique identifier for the survey. | |
| | all_cites | List of document IDs cited in the survey. | |
| | Bertopic_CD | A diversity measure computed using BERTopic. | |
|
|
| #### Literature Knowledge Base |
|
|
| The corpus containing all literature articles is available in: `data/corpus.json` |
|
|
| **Example** : Here, we present how articles are organized in the knowledge base. Overly long abstract has been appropriately shortened. |
|
|
| ``` |
| { |
| "Title": "Information Geometry of Evolution of Neural Network Parameters While Training", |
| "Authors": [ |
| "Abhiram Anand Thiruthummal", |
| "Eun-jin Kim", |
| "Sergiy Shelyag" |
| ], |
| "Year": "2024", |
| "Date": "2024-06-07T23:42:54Z", |
| "Abstract": "Artificial neural networks (ANNs) are powerful tools capable of approximating any arbitrary mathematical function, but their interpretability remains limited...", |
| "Category": "cs.LG", |
| "doc_id": 1086990 |
| } |
| ``` |
|
|
|
|
|
|
| The following are explanations of each field: |
|
|
| | Key | Description | |
| | -------- | --------------------------------------------------------- | |
| | Title | The title of the research paper. | |
| | Authors | A list of contributing researchers. | |
| | Year | The publication year of the paper. | |
| | Date | The exact timestamp of the paper’s release. | |
| | Abstract | The abstract of the paper. | |
| | Category | The subject classification following the arXiv taxonomy. | |
| | doc_id | A unique identifier assigned for reference and retrieval. | |
| |
| #### Auxiliary Mappings: |
| |
| The mapping containing all queries and their corresponding articles is available in: `data/queries.json` |
| |
| Each query in `data/queries.json` corresponds to a section or paragraph from the ground truth surveys with high citation extraction quality. The associated articles are the references cited in that part of the survey. |
| |
| Below is an example, Overly long content has been appropriately shortened. |
| |
| ``` |
| { |
| "original_id": "23870233-7f5b-4ef1-9d38-e6f3adb0fa48", |
| "query_id": 486, |
| "date": "2020-07-16T09:23:13Z", |
| "year": "2020", |
| "category": "cs.LG", |
| "content": "}\n{\nMachine learning classifiers can perpetuate and amplify the existing systemic injustices in society . Hence, fairness is becoming another important topic. Traditionally...", |
| "prefix_titles": [ |
| [ |
| "title", |
| "Learning from Noisy Labels with Deep Neural Networks: A Survey" |
| ], |
| [ |
| "section", |
| "Future Research Directions" |
| ], |
| [ |
| "subsection", |
| "{Robust and Fair Training" |
| ] |
| ], |
| "prefix_titles_query": "What are the future research directions for robust and fair training in the context of learning from noisy labels with deep neural networks?", |
| "cites": [ |
| 7771, |
| 4163, |
| 3899, |
| 8740, |
| 8739 |
| ], |
| "cite_extract_rate": 0.8333333333333334, |
| "origin_cites_number": 6 |
| } |
| ``` |
| |
|
|
|
|
| The following are explanations of each field: |
|
|
| | Key | Description | |
| | ------------------- | ------------------------------------------------------------ | |
| | original_id | The identifier for the section where this query is from. | |
| | query_id | The ID associated with the specific query. | |
| | content | The content of the section. | |
| | prefix_titles | A hierarchical list of titles of the section/subsection/paragraph | |
| | prefix_titles_query | The question this passage is relevant to. The goal of the question is to retrieve relevant documents. | |
| | cites | A list of document IDs that are cited within this section. | |
| | cite_extract_rate | The ratio of extracted citations to the total number of citations in the original document. | |
| | origin_cites_number | The total number of citations originally present in the section. | |
| |
| **Note**: Not every section in the ground truth surveys has a corresponding entry in `queries.json`. Only sections with a high citation extraction rate are included. |
| These entries can be used to train retrieval models, where `prefix_titles_query` serves as the query and `cites` contains the relevant document IDs. |
| The data in `queries.json` has not been pre-split into training and development sets—you may divide it manually as needed. |
| |
| |
| |
| |
| ## License |
| |
| This project is licensed under [MIT License](https://github.com/oneal2000/SurGE/blob/main/LICENSE). Please review the LICENSE file for more details. |
| |
| |
| --- |
| license: mit |
| task_categories: |
| - text-generation |
| language: |
| - en |
| --- |
