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metadata
dataset_info:
  features:
    - name: id
      dtype: uint64
    - name: s2orc_id
      dtype: uint64
    - name: mag_id
      dtype: uint64
    - name: doi
      dtype: string
    - name: title
      dtype: string
    - name: abstract
      list:
        list:
          - name: title_path
            list: string
          - name: text
            dtype: string
          - name: citations
            list:
              - name: index
                dtype: uint16
              - name: start
                dtype: uint32
              - name: end
                dtype: uint32
          - name: references
            list:
              - name: index
                dtype: uint16
              - name: start
                dtype: uint32
              - name: end
                dtype: uint32
    - name: related_work
      dtype: string
    - name: hierarchy
      dtype: string
    - name: authors
      list: string
    - name: year
      dtype: uint16
    - name: fields_of_study
      list: string
    - name: referenced
      list:
        - name: id
          dtype: uint64
        - name: s2orc_id
          dtype: uint64
        - name: mag_id
          dtype: uint64
        - name: doi
          dtype: string
        - name: title
          dtype: string
        - name: hierarchy
          dtype: string
        - name: authors
          list: string
        - name: year
          dtype: uint16
        - name: fields_of_study
          list: string
        - name: citations
          list: uint64
        - name: bibliography
          list:
            - name: id
              dtype: uint64
            - name: title
              dtype: string
            - name: year
              dtype: uint16
            - name: authors
              list: string
        - name: non_plaintext_content
          list:
            - name: type
              dtype: string
            - name: description
              dtype: string
    - name: bibliography
      list:
        - name: id
          dtype: uint64
        - name: title
          dtype: string
        - name: year
          dtype: uint16
        - name: authors
          list: string
    - name: non_plaintext_content
      list:
        - name: type
          dtype: string
        - name: description
          dtype: string
  splits:
    - name: train
      num_bytes: 39235598318
      num_examples: 91445
    - name: validation
      num_bytes: 581643389
      num_examples: 1127
    - name: test
      num_bytes: 965353630
      num_examples: 1878
  download_size: 15174246190
  dataset_size: 40782595337
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*

OARelatedWork

OARelatedWork is a large-scale multi-document summarization dataset for related work generation containing whole related work sections and full-texts of cited papers. The dataset includes 94 450 papers and 5 824 689 unique referenced papers.

Split Samples
Train 91,445
Validation 1,127
Test 1,878

Fields

  • id - id from our corpus
  • s2orc_id - SemanticScholar id
  • mag_id - Microsoft Academic Graph id
  • DOI - Might be DOI for another version of document than the one used for processing.
  • title - title of publication
  • abstract - list of paragraphs in an abstract, every paragraph is a list of sentences
  • related_work - The target related work section. The format differs according to used configuration.
  • hierarchy - Document body, but the abstract and related work section. The format differs according to used configuration.
  • authors - authors of publication
  • year - year of publication
  • fields_of_study - list of fields of study
  • referenced - List of referenced document. Each referenced document has the same fields, but the abstract, related_work, and referenced field are missing. All references have the abstract section as a first section in hierarchy.
  • bibliography - document bibliography
  • non_plaintext_content - tables and figures

Structure

We provide multiple dataset configurations to make working with this dataset as simple as possible. Also, by the time this dataset is released, it is not possible to use hierarchical structures, which we use to represent document content. Thus, we used several workarounds, such as flattening the hierarchy or using a JSON representation of hierarchy.

We divide a document content into sections, subsections, paragraphs, and sentences. Not all documents have full text and subsections.

Flattened hierarchy

The hierarchy is flattened on section level. meaning that it is a list of (sub)sections. Each(sub)section is represented by list of titles on tree path to given section and list of paragraphs in given (sub)section. Each paragraph is represented as a list of sentences. Every sentence also contains metadata such as citation spans.

Configurations

  • oa_related_work

    uses JSON format to represent hierarchy

  • abstracts

    provides just abstracts of cited papers, hierarchy of target paper is flattened

  • flattened_sections

    hierarchy is flattened, see the Flattened hierarchy section above

  • greedy oracle based configurations

    These configurations provide filtered content using greedy oracle. Since the greedy oracle is a cheating baseline, use these with care.

    • greedy_oracle_sentences

      Each referenced document is represented by sentences that are in greedy extractive oracle summary. It is using same format as flattened_sections.

    • greedy_oracle_paragraphs

      Each referenced document is represented by paragraphs that contain sentences that are in greedy extractive oracle summary. It is using same format as flattened_sections.

    • greedy_oracle_per_input_doc_sentences

      Each referenced document is represented by sentences that are in greedy extractive oracle summary done on each document separately. It is using same format as flattened_sections.

    • greedy_oracle_per_input_doc_paragraphs

      Each referenced document is represented by paragraphs that contain sentences that are in greedy extractive oracle summary done on each document separately. It is using same format as flattened_sections.

    • abstracts_with_greedy_oracle_target_sentences

      Same as abstracts, but target is greedy oracle summary of target document. Target document is the one for which the related work is generated for.

I don't want to use Hugging Face loader

Because the processing (cache creation) by Hugging Face loader is slow, we also provide our custom loader that is available at https://github.com/KNOT-FIT-BUT/OAPapersLoader.

TUI Viewer

We provide a TUI viewer with the dataset (https://github.com/KNOT-FIT-BUT/OAPapersViewer), as it is difficult to navigate data of this kind, especially when one wants to investigate the content of cited papers.

TUI Viewer

Sources

The dataset contains open access papers obtained from CORE and SemanticScholar corpora. These corpora contain third party content and materials, such as open access works from publicly available sources. In addition to the licenses of those organizations (ODC-By, CC BY-NC), any underlying Third Party Content may be subject to separate license terms by the respective third party owner. We made the best effort to provide identifiers (title, authors, year, DOI, or SemanticScholar ID) of collected papers to allow the user of this dataset to check the license.