Earticle

초록

영어

Summaries or abstracts available with medical articles are useful for the physicians, medical students and patients to know rapidly what is the article about and decide whether articles are suitable for in-depth study. Since all medical text documents do not come with author written abstracts or summaries, an automatic medical text summarization system can facilitate rapid medical information access on the web. We approach the problem of automatically generating summary from medical article as a supervised learning task. We treat a document as a set of sentences, which the learning algorithm must learn to classify as positive or negative examples of sentences based on summary worthiness of the sentences. We apply the machine learning algorithm called bagging to this learning task, where a C4.5 decision tree has been chosen as the base learner. We also compare the proposed approach to some existing summarization approaches.

목차

Abstract
 1. Introduction
 2. Related work
 3. Domain knowledge preparation
 4. Summarization method
  4.1. Document preprocessing
  4.2 Using Bagging for sentence extraction
  4.3 Summary generation
 5. Comparison to an existing summarizer
 6. Evaluation, experimental results and discussion
  6.1. Evaluation
  6.2. Results
  6.3. Discussion
  6.4. Future work and limitations
 7. Conclusion
 References

키워드

저자

• Kamal Sarkar [ Computer Science and Engineering Department, Jadavpur University ]
• Mita Nasipuri [ Computer Science and Engineering Department, Jadavpur University ]
• Suranjan Ghose [ Computer Science and Engineering Department, Jadavpur University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605