Test summarization using relevance measures and latent semantic analysis

A system for creating a generic text summary of a document;

• said system comprising:
• a computer comprising at least a CPU and a memory;
• an interface for obtaining the document;
• a display for displaying said generic text summary;
• and
• summarizer program code, operable on said computer, for analyzing and summarizing said document;
• said summarizer program code comprising:
• a vector generator for creating a weighted document term-frequency vector for said document and creating a weighted sentence term-frequency vector for each sentence in said document;
• a scoring engine for computing a score for each said weighted sentence term-frequency vector in accordance with relevance to said weighted document term-frequency vector;
• a selector for selecting a sentence for inclusion in said generic text summary in accordance with output results from said scoring engine;
• a document editor for deleting said selected sentence from said document and for eliminating terms in said selected sentence from said document and;
• a generic summary generator for generating the generic text summary based on the selected sentence.

A method of creating a generic text summary of a document;

• said method comprising:
• obtaining the document;
• decomposing said document into individual sentences;
• forming a candidate sentence set from said individual sentences;
• for each of said individual sentences in said candidate sentence set, creating a weighted sentence term-frequency vector;
• creating a weighted document term-frequency vector for said document;
• for each of said individual sentences in said candidate sentence set, computing a relevance score for said weighted sentence term-frequency vector relative to said weighted document term-frequency vector;
• selecting a sentence for inclusion in said generic text summary in accordance with said computing, wherein the selected sentence has the computed relevance score representing a high degree of relevance of the corresponding weighted sentence term-frequency vector to said weighted document term-frequency vector;
• deleting said selected sentence from said candidate sentence set;
• eliminating terms in said selected sentence from said document;
• recreating said weighted document term-frequency vector in accordance with said deleting and said eliminating;
• and
• generating the generic text summary based on the selected sentence.