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null
Blogging birds: Generating narratives about reintroduced species to promote public engagement
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"[{'title_path': ['2 Related work'], 'paragraphs': [[{'title_path': ['2 Related work', '[0]', '[0]'](...TRUNCATED)
"{\"headline\": \"Blogging birds: Generating narratives about reintroduced species to promote public(...TRUNCATED)
[ "Advaith Siddharthan", "Matthew Green", "Kees Van Deemter", "Chris Mellish", "René Van Der Wal" ]
2,012
[ "Computer science", "Narrative", "Public engagement", "World Wide Web", "Public relations" ]
[{"id":2312037,"s2orc_id":36379,"mag_id":2112824353,"doi":"10.1016/J.ARTINT.2008.12.002","title":"Au(...TRUNCATED)
[{"id":7162679,"title":"Using nlg and sensors to support personal narrative for children with comple(...TRUNCATED)
[{"type":"figure","description":"Figure 1 : Figure 1: Plot of (a) distance from nest as a function o(...TRUNCATED)
95
54,955,648
180,360,333
null
Designing a Mobile Device for Pre-hospital Care
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"[{'title_path': ['2. RELATED WORK'], 'paragraphs': [[{'title_path': ['2. RELATED WORK', '[0]', '[0](...TRUNCATED)
"{\"headline\": \"Designing a Mobile Device for Pre-hospital Care\", \"content\": [{\"headline\": \"(...TRUNCATED)
["Anne Schneider","Nirwan Sharma","Alasdair Mort","Chris Mellish","Ehud Reiter","Phil Wilson","Alasd(...TRUNCATED)
2,013
["Multimedia","Natural language generation","User interface design","Computer science","Mobile devic(...TRUNCATED)
[{"id":2210266,"s2orc_id":1665288,"mag_id":2090818911,"doi":"10.1016/S0004-3702(02)00370-3","title":(...TRUNCATED)
[{"id":12750575,"title":"Text-background polarity affects performance irrespective of ambient illumi(...TRUNCATED)
[{"type":"figure","description":"Figure 1 : Figure 1: First hardware prototype (Getac Z710 tablet an(...TRUNCATED)
546
19,427,686
2,763,283,821
10.1109/IROS.2017.8206372
Learning How a Tool Affords by Simulating 3D Models from the Web
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"[{'title_path': ['II. RELATED WORK'], 'paragraphs': [[{'title_path': ['II. RELATED WORK', '[0]', '[(...TRUNCATED)
"{\"headline\": \"Learning How a Tool Affords by Simulating 3D Models from the Web\", \"content\": [(...TRUNCATED)
[ "Paulo Abelha", "Frank Guerin" ]
2,017
["Process (engineering)","Function (engineering)","Set (psychology)","Object (computer science)","GR(...TRUNCATED)
[{"id":4438,"s2orc_id":8583283,"mag_id":2412396384,"doi":"10.1109/ICRA.2016.7487400","title":"A Mode(...TRUNCATED)
[{"id":10426079,"title":"Affordance detection of tool parts from geometric features","year":2015,"au(...TRUNCATED)
[{"type":"figure","description":"Fig. 2 : Fig. 2: Overview of the system; how it is trained and used(...TRUNCATED)
682
54,819,100
1,529,315,033
null
Using NLG to Manage Information in Medical Emergencies
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"[{'title_path': ['2. RELATED WORK'], 'paragraphs': [[{'title_path': ['2. RELATED WORK', '[0]', '[0](...TRUNCATED)
"{\"headline\": \"Using NLG to Manage Information in Medical Emergencies\", \"content\": [{\"headlin(...TRUNCATED)
["Hien Nguyen","Peter Kindness","Chris Mellish","Jonathan Knight","Alasdair Mort","Ehud Reiter","Ala(...TRUNCATED)
2,011
["First aid","Handover","Natural language generation","Rural area","Family medicine","Medicine","Med(...TRUNCATED)
[{"id":2312037,"s2orc_id":36379,"mag_id":2112824353,"doi":"10.1016/J.ARTINT.2008.12.002","title":"Au(...TRUNCATED)
[{"id":22858919,"title":"Automatic pre-hospital vital signs waveform and trend data capture fills qu(...TRUNCATED)
[{"type":"figure","description":"OF INJURIES: He was hit on his right side by a car. The car was tra(...TRUNCATED)
1,091
17,390,125
1,528,423,985
null
Contract Formation through Preemptive Normative Conflict Resolution
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"[{'title_path': ['6 Related Work'], 'paragraphs': [[{'title_path': ['6 Related Work', '[0]', '[0]'](...TRUNCATED)
"{\"headline\": \"Contract Formation through Preemptive Normative Conflict Resolution\", \"content\"(...TRUNCATED)
[ "Wamberto W Vasconcelos", "Timothy J Norman" ]
2,009
["Mechanism (sociology)","Law and economics","Conflict resolution","Software agent","Normative","Sco(...TRUNCATED)
[{"id":4951,"s2orc_id":1878900,"mag_id":2093419986,"doi":"10.1007/S10458-008-9059-4","title":"Constr(...TRUNCATED)
[{"id":25710700,"title":"From Logic Programming to Prolog","year":1997,"authors":["K R Apt"]},{"id(...TRUNCATED)
[{"type":"figure","description":"Fig. 1. Fig. 1. Check if Action is within Influence of a Norm "},{(...TRUNCATED)
1,173
12,480,959
2,215,408,773
null
Agent Support for Human Team Collaboration in Uncertain Environments
[[{"title_path":["Agent Support for Human Team Collaboration in Uncertain Environments","Abstract","(...TRUNCATED)
"[{'title_path': ['7 Related work'], 'paragraphs': [[{'title_path': ['7 Related work', '[0]', '[0]'](...TRUNCATED)
"{\"headline\": \"Agent Support for Human Team Collaboration in Uncertain Environments\", \"content\(...TRUNCATED)
[ "Daniele Masato", "Timothy J Norman", "Wamberto W Vasconcelos", "Wamberto W M P D Vasconcelos" ]
2,009
["Software agent","Action (philosophy)","Task (project management)","Synchronization (computer scien(...TRUNCATED)
[{"id":6194899,"s2orc_id":3338960,"mag_id":2139989336,"doi":"10.1145/1082473.1082616","title":"Exten(...TRUNCATED)
[{"id":11470931,"title":"Human-Machine Collaborative Planning","year":2002,"authors":["J Allen","G (...TRUNCATED)
[ { "type": "figure", "description": "Fig. 1. Fig. 1. System overview " } ]
2,233
15,680,771
2,124,248,900
10.1007/978-3-642-41338-4_29
Utilising Provenance to Enhance Social Computation
[[{"title_path":["Utilising Provenance to Enhance Social Computation","Abstract","[0]","[0]"],"text"(...TRUNCATED)
"[{'title_path': ['2 Related Work'], 'paragraphs': [[{'title_path': ['2 Related Work', '[0]', '[0]'](...TRUNCATED)
"{\"headline\": \"Utilising Provenance to Enhance Social Computation\", \"content\": [{\"headline\":(...TRUNCATED)
[ "Milan Markovic", "Peter Edwards", "David Corsar" ]
2,013
["Computer science","Semantic Web","Social Semantic Web","Selection (linguistics)","Computational in(...TRUNCATED)
[{"id":1217,"s2orc_id":null,"mag_id":2122805915,"doi":"10.1016/J.WEBSEM.2011.05.001","title":"Enhanc(...TRUNCATED)
[{"id":null,"title":"Expertise retrieval. Foundations and Trends in Information Retrieval","year":20(...TRUNCATED)
[{"type":"figure","description":"Fig. 2. An example provenance record describing the process of work(...TRUNCATED)
2,443
6,910,648
2,017,710,739
10.1109/ICDM.2014.64
Hete-CF: Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations
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"[{'title_path': ['V. RELATED WORK'], 'paragraphs': [[{'title_path': ['V. RELATED WORK', '[0]', '[0](...TRUNCATED)
"{\"headline\": \"Hete-CF: Social-Based Collaborative Filtering Recommendation using Heterogeneous R(...TRUNCATED)
[ "Chen Luo", "Wei Pang", "Zhe Wang", "Chenghua Lin" ]
2,014
[ "Social network", "Computer science", "Data mining", "Collaborative filtering", "Machine learning" ]
[{"id":6522328,"s2orc_id":3959290,"mag_id":2070700141,"doi":"10.1145/2507157.2507230","title":"Recom(...TRUNCATED)
[{"id":82816763,"title":"Social collaborative filtering by trust","year":2013,"authors":["B Yang","Y(...TRUNCATED)
[{"type":"figure","description":"The work described here was funded by the National Natural Science (...TRUNCATED)
2,757
11,030,842
2,103,491,965
10.1145/2661829.2662003
Truth Discovery in Crowdsourced Detection of Spatial Events
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"[{'title_path': ['8. RELATED WORK'], 'paragraphs': [[{'title_path': ['8. RELATED WORK', '[0]', '[0](...TRUNCATED)
"{\"headline\": \"Truth Discovery in Crowdsourced Detection of Spatial Events\", \"content\": [{\"he(...TRUNCATED)
[ "Robin Wentao Ouyang", "Mani Srivastava", "Alice Toniolo", "Timothy J Norman" ]
2,014
["Probabilistic logic","Quality (business)","Reliability (computer networking)","Data science","Crow(...TRUNCATED)
[{"id":975074,"s2orc_id":8837716,"mag_id":2950094974,"doi":null,"title":"A Bayesian Approach to Disc(...TRUNCATED)
[{"id":9081778,"title":"Location privacy in pervasive computing. Pervasive Computing","year":2003,"a(...TRUNCATED)
[{"type":"figure","description":"Figure 1 : Figure 1: (a) Example user interface for task instructio(...TRUNCATED)
3,134
129,720,101
2,079,070,291
10.1080/14702541.2014.923579
"SHORELINE CHANGE AND COASTAL VULNERABILITY CHARACTERIZATION WITH LANDSAT IMAGERY: A CASE STUDY IN T(...TRUNCATED)
[[{"title_path":["SHORELINE CHANGE AND COASTAL VULNERABILITY CHARACTERIZATION WITH LANDSAT IMAGERY: (...TRUNCATED)
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[ "Cristina Gómez", "Michael A Wulder", "Alastair G Dawson", "William Ritchie", "David R Green" ]
2,014
["Shore","Flooding (psychology)","Change detection","Geography","Geomorphology","Progradation","Phys(...TRUNCATED)
[{"id":12010901,"s2orc_id":15035206,"mag_id":3021495136,"doi":"10.1109/36.536540","title":"Accurate (...TRUNCATED)
[{"id":null,"title":"Loch Carnan) 11/09","year":1989,"authors":["Minch"]},{"id":null,"title":"Betwee(...TRUNCATED)
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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

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.

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