Estimating Contemporary Relevance of Past News

Our society generates massive amounts of digital data, significant portion of which is being archived and made accessible to the public for the current and future use. In addition, historical born-analog documents are being increasingly digitized and included in document archives which are available online. Professionals who use document archives tend to know what they wish to search for. Yet, if the results are to be useful and attractive for ordinary users they need to contain content which is interesting and familiar. However, the state-of-the-art retrieval methods for document archives basically apply same techniques as search engines for synchronic document collections. In this paper, we introduce a novel concept of estimating the relation of archival documents to the present times, called contemporary relevance. Contemporary relevance can be used for improving access to archival document collections so that users have higher probability of finding interesting or useful content. We then propose an effective method for computing contemporary relevance degrees of news articles using Learning to Rank with a range of diverse features, and we successfully test it on the New York Times Annotated document collection. Our proposal offers a novel paradigm of information access to archival document collections by incorporating the context of contemporary time.