--- license: cc-by-4.0 task_categories: - text-classification language: - en tags: - SDQP - scholarly - citation_count_prediction - review_score_prediction dataset_info: features: - name: paperhash dtype: string - name: s2_corpus_id dtype: string - name: arxiv_id dtype: string - name: title dtype: string - name: abstract dtype: string - name: authors sequence: - name: name dtype: string - name: affiliation struct: - name: laboratory dtype: string - name: institution dtype: string - name: location dtype: string - name: summary dtype: string - name: field_of_study sequence: string - name: venue dtype: string - name: publication_date dtype: string - name: n_references dtype: int32 - name: n_citations dtype: int32 - name: n_influential_citations dtype: int32 - name: introduction dtype: string - name: background dtype: string - name: methodology dtype: string - name: experiments_results dtype: string - name: conclusion dtype: string - name: full_text dtype: string - name: decision dtype: bool - name: decision_text dtype: string - name: reviews sequence: - name: review_id dtype: string - name: review struct: - name: title dtype: string - name: paper_summary dtype: string - name: main_review dtype: string - name: strength_weakness dtype: string - name: questions dtype: string - name: limitations dtype: string - name: review_summary dtype: string - name: score dtype: float32 - name: confidence dtype: float32 - name: novelty dtype: float32 - name: correctness dtype: float32 - name: clarity dtype: float32 - name: impact dtype: float32 - name: reproducibility dtype: float32 - name: ethics dtype: string - name: comments sequence: - name: title dtype: string - name: comment dtype: string - name: references sequence: - name: paperhash dtype: string - name: title dtype: string - name: abstract dtype: string - name: authors sequence: - name: name dtype: string - name: affiliation struct: - name: laboratory dtype: string - name: institution dtype: string - name: location dtype: string - name: arxiv_id dtype: string - name: s2_corpus_id dtype: string - name: intents sequence: string - name: isInfluential dtype: bool - name: hypothesis dtype: string - name: month_since_publication dtype: int32 - name: avg_citations_per_month dtype: float32 splits: - name: acl_ocl_train num_bytes: 2552087355 num_examples: 42060 - name: acl_ocl_validation num_bytes: 758528471 num_examples: 9013 - name: acl_ocl_test num_bytes: 853299931 num_examples: 9014 download_size: 2079805663 dataset_size: 4163915757 configs: - config_name: default data_files: - split: acl_ocl_train path: data/acl_ocl_train-* - split: acl_ocl_validation path: data/acl_ocl_validation-* - split: acl_ocl_test path: data/acl_ocl_test-* --- Datasets related to the task of Scholarly Document Quality Prediction (SDQP). Each sample is an academic paper for which either the citation count or the review score can be predicted (depending on availability). The information that is potentially available for each sample can be found below. ## ACL-OCL Extended A dataset for citation count prediction only, based on the [ACL-OCL dataset](https://huggingface.co/datasets/WINGNUS/ACL-OCL/tree/main). Extended with updated citation counts, references and annotated research hypothesis ## OpenReview A dataset for review score and citation count prediction, obtained by parsing OpenReview. Due to licensing the dataset comes in 3 formats: 1. openreview-public: Contains full information on all OpenReview submissions that are accompanied by a license. 2. openreview-full-light: The full dataset excluding the parsed pdfs of the submitted papers. 3. openreview-full: A script to obtain the full dataset with submissions. ## Citation If you use the dataset in your work, please cite: The data model for the papers: ### Paper Data Model ```json { # ID's "paperhash": str, "arxiv_id": str | None, "s2_corpus_id": str | None, # Basic Info "title":str, "authors": list[Author], "abstract": str | None, "summary": str | None, "publication_date": str | None, # OpenReview Metadata "field_of_study": list[str] | str | None, "venue": str | None, # s2 Metadata "n_references": int | None, "n_citations": int | None, "n_influential_citations": int | None, "open_access": bool | None, "external_ids": dict | None, "pdf_url": str | None, # Content "parsed_pdf": dict | None, "parsed_latex": dict | None, "structured_content": dict[str, Section], # Review Data "openreview": bool, "decision": bool | None, "decision_text": str | None, "reviews": list[Review] | None, "comments": list[Comment] | None, # References "references": list[Reference] | None, "bibref2section": dict, "bibref2paperhash": dict, # Hypothesis "hypothesis": dict | None } ``` ### Author Data Model ```json { "name":str, "affiliation": { "laboratory": str | dict | None, "institution": str | dict | None, "location": str | dict | None } } ``` ### Reference Data Model ```json { "paperhash": str, "title": str, "abstract": str = "", "authors": list[Author], # IDs "arxiv_id": str | None, "s2_corpus_id": str | None, "external_ids": dict| None, # Reference specific info "intents": list[str] | None = None, "isInfluential": bool | None = None } ``` ### Comment Data Model ```json { "title": str, "comment": str } ``` ### Section Data Model ```json { "name": str, "sec_num": str, "classification": str, "text": str, "subsections": list[Section] } ``` ### Review Data Model ```json { "review_id": str, "review": { "title": str | None, "paper_summary": str | None, "main_review": str | None, "strength_weakness": str | None, "questions": str | None, "limitations": str | None, "review_summary": str | None } "score": float | None, "confidence": float | None, "novelty": float | None, "correctness": float | None, "clarity": float | None, "impact": float | None, "reproducibility": float | None, "ethics": str | None } ```