Extracting Keyphrases from Individual News Articles

Extraction of keyphrases from individual documents is a research area in which one try to extract a small set of keyphrases that describe the content of a single document. The advantages with this form of extraction is that it retains most of the semantic context from the document.In this thesis we focus on the news article domain and use the structure of a news article to improve the quality of the extracted keyphrases. An existing individual document keyphrase extraction algorithm is used as the basis. This algorithm is enhanced by implementing a weighting system based upon the structure of news articles. In addition some other common methods for keyword extraction is applied. The effects of these changes are tested extensively in the evaluation phase.In the evaluation of the implemented prototype we find that the introduction of a weight based system yields results that are equal to the basic algorithm and that few improvements can be made. We do however find that an automatically generated stopword list based on the corpus improves the results by 1-2%.