Abstract: Examples associated with identifying documents are disclosed. One example includes identifying at least one document in a corpus of documents that contains at least one token. The token is identified from a search query. Relevance of the search query to each identified document is determined according to a Helmholtz score for each respective identified token.

Abstract: Examples relate to providing keywords to generate policy conditions. Examples include a computing device to remove, from a corpus of documents, words that are common among classes in the corpus to create a reduced corpus. In some examples, the computing device is to identify a set of keywords for a particular one of the classes in the reduced corpus by identifying keywords that are common among documents in the particular class, and provide the set of keywords to generate a policy condition.

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).

Abstract: Example embodiments relate to keyword determination based on a weight of meaningfulness. In example embodiments, a computing device may determine a number of occurrences of a word in a particular document and may then determine a weight of meaningfulness for the word based on the number of occurrences. The computing device may then add the word to a set of keywords for the document based on the weight of meaningfulness.

Abstract: Example embodiments relate to keyword determination based on a weight of meaningfulness. In example embodiments, a computing device may determine a number of occurrences of a word in a particular document and may then determine a weight of meaningfulness for the word based on the number of occurrences. The computing device may then add the word to a set of keywords for the document based on the weight of meaningfulness.