This document summarizes a workshop on automatic bibliographic metadata extraction. It discusses the goals of metadata extraction, including improving efficiency of manual metadata creation and revision. Use cases presented include suggesting metadata to researchers during paper deposit and analyzing citations to assess research impact. Standards around document formats, metadata schemas, and citation styles were highlighted as important to metadata extraction. Existing tools for extracting metadata from various document formats were also presented. Issues around text accessibility, localization, and imperfect suggestions were noted. Recommendations included developing a customizable and retrainable system to simplify metadata operations and identification of incorrect records.

1. Bibliographic metadata (including citation) Tuesday 7 th July 2009 AMG 2 nd workshop, University of Leicester , Leicester www.bath.ac.uk UKOLN is supported by: Alexey Strelnikov Research Officer UKOLN Contributions from Emma Tonkin

3. What and why

4. Use cases

5. Key points

6. Issues

7. Recommendations

10. For example:

11. Title

12. Authors

13. Emails

14. Citations

16. Citation and reference analysis – usually involves regular expressions

17. Might involve visual structure analysis and text mining

19. Revision of metadata

20. Comparison and aggregation

21. <Put your own operation here>

23. It is not a drop-in replacement for manual creation, but semi-automated feature extraction can make for better metadata quality overall

25. Instead of fully manual deposit (typing in all values) he makes use of system suggestions, which make the process faster and simpler

27. How many citations of my work?

28. Network of citations (existing system: Google scholar, citeseer.net...)

30. Make use of system recommendations, and a generated value confidence level

31. Easier to find invalid or obsolete metadata values

33. Have application profile, but no feedback on how it is followed

37. Automated metadata extraction would be an improvement, but not replacement

38. Service is invisible , it just makes suggestions: for example – 'the metadata field “title” should be “Some name”'

41. Metadata encoding

42. Locale specifics

43. Citation formats

48. SWAP

49. LOM

50. OAI-ORE

51. MARC

55. mm/dd/yyyy

58. MLA – literature and the arts

59. AMA - biology

60. Turabian – multi-field

61. Chicago standard – publications

62. Harvard, Numerical, MHRA - multi-field

64. Helps to overcome standards heterogeneity

66. DepositPlait (var. format metadata -> metadata repository)

67. DataFountains (var. format->metadata)

68. paperBase (prototype concentrating on eprint documents)

70. Readability of the text

71. Legal issues

72. Engineering constraints - machine suggestions might be imperfect

73. Language & localization - need to retrain system for the other locale

76. Classify paper nature