Abstract: An embodiment includes identifying, from among the plurality of digital content datasets, a set of candidate textual items based on relevance to a specified subtopic using one or more natural language processing techniques. The embodiment groups candidate textual items into a predetermined number of groups using relevance scores and feature vectors. The embodiment trains a pre-trained encoder-decoder model using a designated group of selected textual items, where the pre-trained encoder-decoder model is pretrained to generate textual content according to a particular style of writing. The embodiment generates, using the pre-trained encoder-decoder model, machine-authored textual content in the particular style of writing resulting in an article about the specified subtopic based on the designated group of selected textual items. The embodiment also includes transmitting the article to a remote web server as an update for a website.

Abstract: An embodiment calculates a point of view (POV) value for a subportion of an item of media content, including evaluating user inputs related to an automatically detected POV of the subportion of the item. The embodiment also calculates a non-factual cost for the subportion of the item indicative of an amount of the subportion of the item that lacks support in a fact-based corpora. The embodiment also performs a cascaded summarization process comprising generating a summary of the item using the subportion of the item and a summarization technique, analyzing the summary using a fact-checking algorithm to determine whether the summary satisfies a factual score threshold, and performing a next iteration of the cascaded summarization process while the summary fails to satisfy the factual score threshold. The embodiment also communicates a summary satisfying the factual score threshold to a user device.

Abstract: A relevant factoid(s) related to multimedia data is generated by splitting a multimedia item into a media component and a text component. Text information is retrieved relevant to text data from the text component using a query. The text information is summarized into a factoid. Source data is checked for an image based on the multimedia component. A current state image is generated from the image. The factoid and the current state image are combined into a combined factoid, and the combined factoid is stored for sending to a media outlet for presentation on a media format.

Abstract: A computer receives a multimedia data, where the multimedia data comprises a plurality of frames. The computer converts the multimedia data into a signal wave having a plurality of frequencies and a plurality of amplitudes. The computer determines a frame from the plurality of frames having a pronoun. The computer identifies a topic of the frame. The computer searches for a frame in a media repository having a highest correlation coefficient with the topic of the frame, where the frame from the media repository comprises a bag of objects and resolves the anaphora disambiguation by substituting the pronoun with an object from the bag of objects.

Abstract: An embodiment includes identifying, from among the plurality of digital content datasets, a set of candidate textual items based on relevance to a specified subtopic using one or more natural language processing techniques. The embodiment groups candidate textual items into a predetermined number of groups using relevance scores and feature vectors. The embodiment trains a pre-trained encoder-decoder model using a designated group of selected textual items, where the pre-trained encoder-decoder model is pretrained to generate textual content according to a particular style of writing. The embodiment generates, using the pre-trained encoder-decoder model, machine-authored textual content in the particular style of writing resulting in an article about the specified subtopic based on the designated group of selected textual items. The embodiment also includes transmitting the article to a remote web server as an update for a website.

Abstract: A method, system, and computer program product for implementing machine learning natural language digital package generation and selection is provided. The method includes receiving from hardware and software sources, factual data associated with an event. In response, natural language digital templates comprising natural language phrase variants for each portion of the factual data is generated. Factual data phrases are generated and packaged into digital packages including at least one natural language phrase variant with respect to each portion of factual data. An initial package is selected by minimizing a number of repetitions of the factual data phrases across the digital packages and digital summaries are extracted. Alignment attributes associated with the digital summaries are determined with respect to the initial package and a final package is selected. A hardware device is enabled for presenting a video stream including the final package with respect to the event.

Abstract: A computer receives a multimedia data, where the multimedia data comprises a plurality of frames. The computer converts the multimedia data into a signal wave having a plurality of frequencies and a plurality of amplitudes. The computer determines a frame from the plurality of frames having a pronoun. The computer identifies a topic of the frame. The computer searches for a frame in a media repository having a highest correlation coefficient with the topic of the frame, where the frame from the media repository comprises a bag of objects and resolves the anaphora disambiguation by substituting the pronoun with an object from the bag of objects.

Abstract: An embodiment calculates a point of view (POV) value for a subportion of an item of media content, including evaluating user inputs related to an automatically detected POV of the subportion of the item. The embodiment also calculates a non-factual cost for the subportion of the item indicative of an amount of the subportion of the item that lacks support in a fact-based corpora. The embodiment also performs a cascaded summarization process comprising generating a summary of the item using the subportion of the item and a summarization technique, analyzing the summary using a fact-checking algorithm to determine whether the summary satisfies a factual score threshold, and performing a next iteration of the cascaded summarization process while the summary fails to satisfy the factual score threshold. The embodiment also communicates a summary satisfying the factual score threshold to a user device.

Abstract: A relevant factoid(s) related to multimedia data is generated by splitting a multimedia item into a media component and a text component. Text information is retrieved relevant to text data from the text component using a query. The text information is summarized into a factoid. Source data is checked for an image based on the multimedia component. A current state image is generated from the image. The factoid and the current state image are combined into a combined factoid, and the combined factoid is stored for sending to a media outlet for presentation on a media format.