# US 2014/0195897 A1 · Text Summarization > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2014/0195897 A1 - Document ID: US20140195897A1 - Status: Published application - Kind: A1 - Application number: 14/235,876 - Filed: 2011-09-20 - Published: 2014-07-10 - Earliest claimed: 2011-09-20 - Inventors: Helen Y. Balinsky; Alexander Balinsky; Steven J. Simske - Web page: https://patentgrep.com/patent/US20140195897A1 ## CPC - G06F 40/106: Display of layout of documents; Previewing - G06F 16/345: {Summarisation for human users} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract Methods, systems, and computer readable media with executable instructions, and/or logic are provided for text summarization. An example method of text summarization can include determining, via a computing system (674), a graph (314) with a small world structure, corresponding to a document (300) comprising text, wherein nodes (316) of the graph (314) correspond to text features (302, 304) of the document (300) and edges (318) between particular nodes (316) represent relationships between the text features (302, 304) represented by the particular nodes (316) (440). The nodes (316) (442) are ranked via the computing system (674), and those nodes (316) having importance in the small world structure (444) are identified via the computing system. Text features (302, 304) corresponding to the indentified nodes (316) are selected, via the computing system (674), as a summary (334) of the document (300) (446). ## Claims ### Claim 1 A method for text summarization, comprising: determining, via a computing system (674), a graph (314) with a small world structure, corresponding to a document (300) comprising text, wherein nodes (316) of the graph (314) correspond to text features (392, 304) of the document (300) and edges (318) between particular nodes (316) represent relationships between the text features (302, 304) represented by the particular nodes (316) (440); ranking, via the computing system (674), the nodes (316) (442); identifying, via the computing system (674), those nodes (316) having importance in the small world structure (444); and selecting, via the computing system (674), text features (302, 304) corresponding to the identified nodes (316) as a summary (334) of the document (300) (446). ### Claim 2 (depends on 1) The method of claim 1, wherein determining a graph (314) with a small world structure includes: determining, via a computing system (674), a parametric family of graphs (314) corresponding to a structure of the document (300) comprising text (440); varying, via the computing system (674), a parameter of the parametric family of graphs (314); identifying, via the computing system (674), at least one graph (314) with a small world structure; and joining nodes (316) representing adjacent text features (302, 304) in the document (300) by an edge (318) and nodes (316) representing text features (302, 304) that include at least one keyword (312-1, 312-2, . . . , 312-N) included in an identified set of keywords (312-1, 312-2, . . . , 312-N) by an edge (318). ### Claim 3 (depends on 2) The method of claim 2, wherein the text features (302, 304) are language structures larger than a paragraph (304) ### Claim 4 (depends on 2) The method of claim 2, wherein ranking the nodes (310) includes ranking the nodes (316) based on the quantity of edges (318) associated with the respective nodes (316), the set of Keywords (312-1, 312-2, . . . , 312-N) being selected using a Helmholtz principle. ### Claim 5 (depends on 1) The method of claim 1, wherein selecting text features as the summary (334) includes: extracting a number of top ranked text features (302, 304) from the document (300); and assembling the number of top ranked text features (302, 304) in the summary (334) according to the ranking of the corresponding node (316). ### Claim 6 (depends on 1) The method of claim 1, wherein selecting text features as the summary (334) includes selecting a highest ranking path in the at least one graph (314) with the small world structure as transitions between the selected text features (302, 304). ### Claim 7 (depends on 1) The method of claim 1, wherein providing the summary (334) includes: receiving input specifying summery (334) length; and determining a quantify of text features (302, 304) to be selected for the summary (334) based on the received input specifying summary (334) length. ### Claim 8 (depends on 7) The method of claim 7, wherein receiving input specifying summary (334) length includes receiving a percentage of the text features (302, 304) comprising the document (300). ### Claim 9 (depends on 7) The method of claim 7, wherein receiving input specifying summary length includes receiving a quantity of text features (302, 304) to include in the summary (334). ### Claim 10 (depends on 1) The method of claim 1, further comprising: determining a range of a parameter for which the graph (314) has a small world structure (444) with a small number of edges, a small mean inter-node distance, and high clustering; selecting a measure of centrality for small world networks; and checking for a corresponding range of the parameter that the measure of centrality has a wide range of values and a heavy-tail distribution. ### Claim 11 (depends on 19) The method of claim 19, wherein ranking the nodes (316) includes sorting the nodes (318) in a decreasing order of the measure of centrality in the small world. ### Claim 12 A non-transitory computer-readable medium (676, 681,684, 795) having computer-readable instructions (682) stored thereon that, if executed by a processor (680, 784), cause the processor (680, 794) to: determine a one-parameter family of graphs (314) corresponding to a structure of a document (300) comprising text; vary a parameter of the one-parameter family of graphs (314); identify at least one graph (314) with a small world structure; rank the text features (302, 304) corresponding to the at least one graph (314) with the small world structure; and provide a summary (334) of the document (300) comprising a number of top ranked text features (302, 304), wherein the parameter is a meaningfulness parameter (324). ### Claim 13 (depends on 12) The non-transitory computer-readable medium (676, 681, 684, 795) of claim 12, further having computer-readable instructions (682) stored thereon that, if executed by the processor (680, 794), cause the processor (680, 794) to: identify a set of keywords (312-1, 312-2, . . . , 312-N) of the document (300) as a function of a meaningfulness parameter (324); represent a graph, wherein nodes (316) of the graph (314) correspond to text features (302, 304) of the document (300) and edges (318) between particular nodes (316) represent relationships between the text features (302, 304) represented by the particular nodes (316); and join nodes (316) representing adjacent text features (302, 304) in the document (300) by an edge (318) and nodes (316) representing text features (302, 304) that include at least one keyword (312-1, 312-2, . . . , 312-N) included in the identified set of keywords (312-1, 312-2, . . . , 312-N) by an edge (318), wherein the meaningfulness parameter (324) is a Helmholtz meaningfulness parameter. ### Claim 14 A computing system (674), comprising: a non-transitory computer-readable medium (676, 681, 684, 795) having computer-readable instructions (682) stored thereon; and a processor (680, 794) coupled to the non-transitory computer-readable medium (676, 681, 684, 795), wherein the processor (680, 794) executes the computer-readable instructions (682) to: determine a one-parameter family of graphs (314) corresponding to a structure of a document (300) comprising text; vary a parameter of the one-parameter family of graphs (314); identify at least one graph (314) with a small world structure; rank the text features (302, 304) corresponding to the at least one graph (314) with the small world structure; and provide a summary (334) of the document (300) comprising a number of top ranked text features (302, 304), wherein the parameter is a Helmholtz meaningfulness parameter (324). ### Claim 15 (depends on 14) The computing system (674) of claim 14, wherein the processor executes the computer-readable instructions to: receive as user input a quantity of text features (302, 304) to include in the summary (334); extract the number of top ranked text features (302, 304) from the document (300); and assemble the number of top ranked text features (302, 304) in the summary (334) according to their respective ranking and the number being based on the received quantity of text features (302, 304).