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Automatic Summarisation for Systematic Reviews using Text Mining (ASSERT)

The JISC funded ASSERT project is a continuation of NaCTeM into the area of social sciences. The overall aim of ASSERT is to encourage greater participation by the social sciences community in e-Research by developing a summarisation service to facilitate the production of systematic reviews and to support a number of community projects related with text mining applications. In particular to:

1. to develop cost-effective and rapid methods for locating relevant studies for input to a systematic review using a combination of text mining techniques;
2. to apply a suite of text mining tools that will support novel methods of information management in the domain of social science systematic reviews (document clustering, information extraction and text summarization);
3. to demonstrate the applicability of the text mining technology in social sciences, in cooperation with the Evidence for Policy and Practice Information and Co-ordinating Centre (EPPI) and the Social Science Research Unit currently heavily involved in producing systematic reviews.

Duration: 2006 - 2008
Funding: £138,000 JISC
Principal Investigator: Sophia Ananiadou
Lead Developer: Brian Rea
Developer of Summarisation and Terminology software: Naoaki Okazaki

Research on this project is carried out in collaboration with the National Centre for e-Social Science (NCeSS) and Evidence for Policy and Practice Information Centre (EPPI).

View a video showing the main functionality of the demonstrator here.

The demonstration of our online searching service for systematic reviews is currently being updated. Minor changes will be made to the interface throughout February 2009, your comments are welcomed.

• Project Summary - What we are doing and why
• Work packages - How the project is being run
• Project Partners - Who is involved
• Reports and Presentations - Deliverables and documentation

Publications

• Ananiadou, S., Okazaki, N., Procter, R., Rea, B. and Thomas, J.(2009). Supporting Systematic Reviews using Text Mining. Social Science Computer Review 27(4), 509-523.
• Sasaki, Y., Rea, B. and Ananiadou, S. (2009). Clinical Text Classification under the Open and Closed Topic Assumptions. International Journal on Data Mining and Bioinformatics (IJDMB) 3(3), 299-313.
• Thomas J. and Ananiadou S (2009). Emerging technologies and methods for research synthesis. Campbell Colloquium.
• Ananiadou, S., Procter, R., Rea, B., Sasaki, Y. and Thomas, J. (2007). Supporting Systematic Reviews using Text Mining. In Proceedings of the 3rd International Conference on e-Social Science.

Tutorials

• Using text mining technologies to support systematic reviews. Workshop at the Joint Colloquium of the Cochrane & Campbell Collaborations, Colorado, 18-22nd October 2010.
• Text mining for systematic reviews. Workshop at the 17th Cochrane Colloquium, Singapore, 11-14th October 2009.