1. A method of summarizing information concerning celebrities, comprising a plurality of steps of: establishing a relational database for holding such information wherein such information contains data selected from the group consisting of: name; gender; and age; gathering one or more stories from a plurality of sources; parsing the stories to determine specific indicators of one or more celebrities; storing said stories in said relational database with appropriate tags to enable retrieval by a user of the database; automatically summarizing the stories based on story text for each celebrity; and presenting said story summaries for viewing by said user; wherein the step of summarizing said stories further comprises a plurality of steps of: generating a corpus of data formed by concatenating all story text associated with a selected celebrity; stripping predetermined incidental words from said corpus of data; generating a plurality of bigrams of the remaining text in the corpus of data; performing term frequency-inverse document frequency analysis on the plurality of bigrams; assigning a weight value to each bigram; and selecting one or more sentences from said corpus of data based on the weighted value of said bigrams.
1. A method of summarizing information concerning celebrities, comprising a plurality of steps of: establishing a relational database for holding such information wherein such information contains data selected from the group consisting of: name; gender; and age; gathering one or more stories from a plurality of sources; parsing the stories to determine specific indicators of one or more celebrities; storing said stories in said relational database with appropriate tags to enable retrieval by a user of the database; automatically summarizing the stories based on story text for each celebrity; and presenting said story summaries for viewing by said user; wherein the step of summarizing said stories further comprises a plurality of steps of: generating a corpus of data formed by concatenating all story text associated with a selected celebrity; stripping predetermined incidental words from said corpus of data; generating a plurality of bigrams of the remaining text in the corpus of data; performing term frequency-inverse document frequency analysis on the plurality of bigrams; assigning a weight value to each bigram; and selecting one or more sentences from said corpus of data based on the weighted value of said bigrams. 4. The method of claim 1 , further comprising a plurality of steps of: generating a corpus of data formed by concatenating all story text associated with a selected celebrity; parsing said corpus of data into parts of speech that can be assigned qualitative values; assigning a qualitative value to each said part of speech; performing term frequency analysis on said corpus of data; and assigning a weighted value to said data based on said qualitative values for each part of speech.