--- 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: openreview_submission_id 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 - name: mean_score dtype: float32 - name: mean_confidence dtype: float32 - name: mean_novelty dtype: float32 - name: mean_correctness dtype: float32 - name: mean_clarity dtype: float32 - name: mean_impact dtype: float32 - name: mean_reproducibility dtype: float32 splits: - name: openreview_public_train num_bytes: 524758562 num_examples: 3773 - name: openreview_public_validation num_bytes: 91750560 num_examples: 808 - name: openreview_public_test num_bytes: 91547171 num_examples: 810 download_size: 348679033 dataset_size: 708056293 configs: - config_name: acl_ocl data_files: - split: train path: data/acl_ocl_train-* - split: validation path: data/acl_ocl_validation-* - split: test path: data/acl_ocl_test-* - config_name: default data_files: - split: openreview_public_train path: data/openreview_public_train-* - split: openreview_public_validation path: data/openreview_public_validation-* - split: openreview_public_test path: data/openreview_public_test-* - config_name: openreview-full data_files: - split: train path: data/openreview_full_training-* - split: validation path: data/openreview_full_validation-* - split: test path: data/openreview_full_test-* - config_name: openreview-iclr data_files: - split: train path: data/iclr_training-* - split: validation path: data/iclr_validation-* - split: test path: data/iclr_test-* - config_name: openreview-neurips data_files: - split: train path: data/neurips_training-* - split: validation path: data/neurips_validation-* - split: test path: data/neurips_test-* - config_name: openreview-public data_files: - split: train path: data/openreview_public_train-* - split: validation path: data/openreview_public_validation-* - split: test path: data/openreview_public_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 different formats: ### Datasets without parsed pdfs of submissions (i.e. the fields introduction, background, methodology, experiments_results, conclusion, full_text are available) 1. **openreview-public**: Contains full information on all OpenReview submissions that are accompanied with a CC BY 4.0 license. ### Datasets without parsed pdfs of submissions (i.e. the fields introduction, background, methodology, experiments_results, conclusion, full_text are None) 1. **openreview-full**: Contains all OpenReview submissions, splits generated based on publications dates. 2. **openreview-iclr**: All ICLR submissions from the years 2018-2023 (training) into 2024 (validation and training). 3. **openreview-neurips**: All NeurIPS submissions from the years 2021-2023 (training) into 2024 (validation and training). ## Citation If you use the dataset in your work, please cite: CC BY 4.0 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 } ```