Document summarization

A non-transitory machine-readable medium having stored thereon machine-executable instructions that when executed by a machine cause the machine to perform a method, the method, comprising:

• determining one or more sentence boundaries in a document to be summarized, a sentence containing one or more terms, a term appearing in one or more sentences;
• establishing a set of terms in the document, a term comprising an n-gram, n being an integer from one to four;
• applying a simplified Porter stemming process to one or more members of the set of terms;
• selectively removing from the set of terms one or more terms that begin with a stop-word;
• selectively removing from the set of terms one or more terms that end with a stop-word;
• selectively removing from the set of terms one or more terms that are stop-words;
• computing a term score for a member of the set of terms according to:
• S(t)=(a−b(sft/N−c)2) * f(ng)
• S(t) being the term score;
• a, b, and c being pre-determined, configurable constants;
• sft being the number of sentences in which term t occurs;
• N being the total number of sentences;
• and
• f(ng) being a function that returns a penalizing value for terms having a single uni-gram, a linearly increasing value for terms having two to four uni-grams, and a constant value for terms having more than four uni-grams;
• computing a sentence score for a member of a set of sentences in the document, the sentence score depending, at least in part, on a length of the sentence measured in uni-grams and a position of the sentence in a paragraph in the document;
• computing a set of entries for a term-sentence matrix that relates members of the set of terms to members of the set of sentences, a value of an entry in the set of entries for the term-sentence matrix depending, at least in part, on a term score and a sentence score;
• computing a dominant topic for the document to be summarized by computing a term eigenvector, where a member of the term eigenvector represents a relevancy of a term to the dominant topic, and by computing a sentence eigenvector, where a member of the sentence eigenvector represents a relevancy of a sentence to the dominant topic;
• simultaneously ranking the set of terms and the set of sentences based, at least in part, on the dominant topic;
• providing a summarization item selected from one or more of, the set of terms, and the set of sentences, the summarization item being selected based, at least in part, on one or more of, a ranking of the set of terms, and a ranking of the set of sentences;
• logically removing one or more members of the set of terms from the set of terms based on a relation with the dominant topic and logically removing one or more members of the set of sentences from the set of sentences based on a relation with the dominant topic;
• and
• determining a subsequent dominant topic.

A system comprising:

• a processor;
• a memory;
• and
• an interface connecting the processor, the memory, and logics, the logics including:
• a term and a sentence logic configured to compute a term score for at least one term, where the term score is based, at least in part on the number of uni-grams in the at least one term, and where the term score is decreased for terms having a single uni-gram and increased linearly for terms having multiple uni-grams and where the term and sentence logic is configured to compute a sentence score, where the sentence score is based, at least in part, on the length of a sentence measured in uni-grams;
• a vector logic configured to produced a term vector related to one or more terms in a text to be summarized and produce a sentence vector related to one or more sentences in the text, a sentence containing one or more terms, and to generated a term eigenvector based, at least in part, on term vector and sentence vector, and producing a sentence eigenvector based, at least in part, on the term vector and the sentence a principal eigenvalue related to the text;
• a dominant topic logic configured to compute a dominant topic for the document, where a member of the term eigenvector represents a relevancy of a term to the dominant topic, and where a member of the sentence eighenvector represents a relevancy of a sentence to the dominant topic;
• a term and sentence ranking logic configured to a term ranking and a sentence ranking based, at least in part, on the principal eigenvalue and the dominant topic;
• and
• a summary logic configured to provide a text summary based, at least in part, on the term ranking and the sentence ranking, the text summary comprising one or more of, a term, and a sentence.