Document Summarization Using Term-Sentence Matrix and HITS Ranking
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Solution Overview
Problem
Existing methods for automatically summarizing documents are inefficient in terms of time and quality, failing to produce summaries that achieve desired recall and precision in applications like search engines and text mining.
Innovation Solution
A method that constructs a term-sentence matrix to relate terms and sentences, using the Hyperlink Induced Topic Search (HITS) algorithm and Single Value Decomposition (SVD) based latent semantic indexing to simultaneously rank terms and sentences, allowing for the identification of dominant topics and extraction of summarization items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If statistical methods, pre-built taxonomy and lexical chain analysis methods, or linguistic methods are used for automatic summarization, then the summarization process can be automated, but the time required to produce summaries of desired quality and recall/precision is excessive
Solution Approach 1:
The patent segments the summarization process into distinct phases: constructing a term-sentence matrix, computing term scores and sentence scores, identifying dominant topics, and selecting summarization items. This segmentation allows for more efficient processing by organizing computations in a structured manner that reduces overall time requirements while maintaining automation.
Solution Approach 2:
The patent changes the approach by using a matrix-based representation (term-sentence matrix) and applying mathematical transformations (eigenvector computation, SVD) to the data. This parameter transformation enables faster identification of dominant topics and summarization items compared to traditional linguistic or statistical methods, thus reducing processing time while maintaining quality.
2Extent of automation
If traditional summarization methods are used, then automation is achieved, but the quality of summarization items (recall and precision) does not meet desired levels
Solution Approach 1:
The patent implements a feedback mechanism through iterative score computation where term scores and sentence scores are computed based on their relationships in the term-sentence matrix. The dominant topic identification and summarization item selection are based on these computed scores, creating a feedback loop that refines the quality of summarization items to achieve desired recall and precision levels.
Solution Approach 2:
The patent replaces traditional linguistic analysis methods with a mathematical approach using matrix operations, eigenvector computation, and singular value decomposition. This substitution of mechanical/linguistic processing with mathematical transformations enables more precise identification of dominant topics and summarization items, thereby improving recall and precision metrics.
3Productivity
If efficient summarization is achieved through matrix-based methods, then time is reduced, but the complexity of the processing system increases
Solution Approach 1:
The term-sentence matrix serves multiple functions: it represents document structure, enables term-sentence relationship analysis, facilitates dominant topic identification, and supports summarization item selection. This multi-functionality of a single data structure reduces the need for multiple separate processing systems, thereby managing complexity while maintaining high productivity.
Solution Approach 2:
The patent introduces a term-sentence matrix as an intermediary data structure that mediates between the raw document and the summarization output. This intermediary enables efficient computation of term and sentence scores, facilitates dominant topic identification, and simplifies the selection of summarization items, thereby improving processing speed while organizing system complexity in a manageable way.
Data Source
AI summary
Systems, methods, and other embodiments associated with automatically summarizing a document are described. One method embodiment includes computing term scores for members of a set of terms in a document to be summarized and computing sentence scores for sentences in a set of sentences in the document. The method embodiment also includes computing a set of entries for a term-sentence matrix that relates terms to sentences. The method embodiment also includes computing a dominant topic for the document and simultaneously ranking the set of terms and the set of sentences based on the dominant topic. The method embodiment provides a summarization item(s) selected from the set of terms and/or the set of sentences.