Abstract: A topic-conditional extractive summarization system identifies the most pertinent sentences in a document for use in a topic summarization. The system utilizes a neural encoder model to generate clusters of similar sentence embeddings for each topic and associated anchors, without ground truth summaries. A graph is generated for each topic, representing the document, and contains a node for each sentence in the document. The edges that connect two sentences contain an edge weight containing a sentence embedding similarity factor and a sentence-topic anchor similarity factor for each connected node. The importance of a sentence for a particular topic is identified by computing a score for each node in a graph that is based on the edge weights of the nodes connected to a particular node.

Abstract: A system embeds source content segments of the source content to generate input vectors of the source content segments and embeds generated content segments of the artificial-intelligence-generated content to generate output vectors of the generated content segments. The system performs a similarity measurement on the input vectors and the output vectors to generate a similarity score for each pair of input vectors and output vectors. The system defines a similarity correspondence between individual content segments of the source content to individual generated content segments of the artificial-intelligence-generated content, based on performing the similarity measurement and outputs the explanation to a user interface device. The explanation indicates generated result correspondences between the individual content segments of the source content and the individual generated content segments of the artificial-intelligence-generated content.

Abstract: Semantic search techniques via focused summarizations are described. For example, a search query is received for a text-based content item in a data set comprising a plurality of text-based content items. A first feature vector representative of the search query is obtained. A respective semantic similarity score is determined between the first feature vector and each of a plurality of second feature vectors. Each of the second feature vectors is representative of a machine-generated summarization of a respective text-based content item. The machine-generated summarization comprises a plurality of multi-word fragments that are selected from the respective text-based content item via a transformer-based machine learning model. A search result is provided responsive to the search query.

Abstract: A comparison engine performs item similarity comparisons. A source item and one or more candidate items are input into a triplet-trained machine learning model trained using training data including triplets of anchor elements, positive elements, and negative elements. Each triplet corresponds to an item included in the training data. The anchor elements and the positive elements are included in the corresponding item. The negative element is included in a different item in the training data. A similarity score between the source item and each of the one or more candidate items is generated from the triplet-trained machine learning model.

Abstract: In one implementation, a device detects multiple vital signs from sensors such as a digital infrared sensor, a photoplethysmogram (PPG) sensor and at least one micro dynamic light scattering (mDLS) sensor, and thereafter in some implementations the vital signs are transmitted to, and stored by, an electronic medical record system.