Abstract: Example methods, apparatus, and systems for generating custom content responsive to a received search query are disclosed. An example method for generating custom content responsive to a received search query includes receiving, via a communication interface from a user computing device, a search query including one or more search terms; determining, responsive to the search query, a set of search results relevant to the search query; identifying, responsive to the search query, third-party content and/or a third party relevant to the search query; generating, based on (i) the search query and (ii) the third-party content or the third party, custom content relevant to the search query and related to a landing page associated with the third-party content or the third party, for presentation along with the set of research results; and transmitting, via the communication interface to the user computing device, the custom content.

Abstract: Example aspects of the present disclosure are directed to systems and methods that employ a machine-learned opinion classification model to classify portions (e.g., sentences, phrases, paragraphs, etc.) of documents (e.g., news articles, web pages, etc.) as being opinions or not opinions. Further, in some implementations, portions classified as opinions can be considered for inclusion in an informational display. For example, document portions can be ranked according to importance and selected for inclusion in an informational display based on their ranking. Additionally or alternatively, for systems which access and consider multiple documents, the portions of a document that are classified as opinion can be compared to similarly-classified portions of other documents to perform document clustering, to ensure diversity within a presentation, and/or other tasks.

Abstract: Example aspects of the present disclosure are directed to systems and methods that employ a machine-learned opinion classification model to classify portions (e.g., sentences, phrases, paragraphs, etc.) of documents (e.g., news articles, web pages, etc.) as being opinions or not opinions. Further, in some implementations, portions classified as opinions can be considered for inclusion in an informational display. For example, document portions can be ranked according to importance and selected for inclusion in an informational display based on their ranking. Additionally or alternatively, for systems which access and consider multiple documents, the portions of a document that are classified as opinion can be compared to similarly-classified portions of other documents to perform document clustering, to ensure diversity within a presentation, and/or other tasks.

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.