Machine-learning based irrelevant sentence classifier

A system comprising:

• one or more processors;
• and
• one or more memories storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
• receiving a narrative data object comprising one or more sentences;
• inputting a sentence, without contextual data from the narrative data object, of the one or more sentences to a binary classification model to determine a binary classification that identifies a general relevance of the sentence, wherein the binary classification comprises either a relevant classification or an irrelevant classification and the binary classification model is trained by:
• (i) receiving (a) a training input comprising a historical narrative data object with a plurality of historical narrative sentences and (b) a training output comprising one or more summary sentences for the historical narrative data object,
• (ii) determining a first labeled historical narrative sentence from the training input by assigning a relevant training label to a first historical narrative sentence of the plurality of historical narrative sentences in response to a first determination that a first maximum relevance score of a first plurality of relevance scores between the first historical narrative sentence and the one or more summary sentences exceeds a score threshold of zero,
• (iii) determining a second labeled historical narrative sentence from the training input by assigning an irrelevant training label to a second historical narrative sentence of the plurality of historical narrative sentences in response to a second determination that a second maximum relevance score of a second plurality of relevance scores between the second historical narrative sentence and the one or more summary sentences fails to exceed the score threshold of zero, and
• (iv) training the binary classification model using the first labeled historical narrative sentence and the second labeled historical narrative sentence from the training input;
• responsive to a determination that the binary classification is the irrelevant classification, generating a pertinent summary by removing the sentence from the narrative data object;
• and
• outputting, based at least in part on the pertinent summary, an output indicia data object for the narrative data object.

One or more non-transitory computer-readable media storing processor-executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• receiving a narrative data object comprising one or more sentences;
• inputting a sentence, without contextual data from the narrative data object, of the one or more sentences to a binary classification model to determine a binary classification that identifies a general relevance of the sentence, wherein the binary classification comprises either a relevant classification or an irrelevant classification and the binary classification model is trained by:
• (i) receiving (a) a training input comprising a historical narrative data object with a plurality of historical narrative sentences and (b) a training output comprising one or more summary sentences for the historical narrative data object,
• (ii) determining a first labeled historical narrative sentence from the training input by assigning a relevant training label to a first historical narrative sentence of the plurality of historical narrative sentences in response to a first determination that a first maximum relevance score of a first plurality of relevance scores between the first historical narrative sentence and the one or more summary sentences exceeds a score threshold of zero,
• (iii) determining a second labeled historical narrative sentence from the training input by assigning an irrelevant training label to a second historical narrative sentence of the plurality of historical narrative sentences in response to a second determination that a second maximum relevance score of a second plurality of relevance scores between the second historical narrative sentence and the one or more summary sentences fails to exceed the score threshold of zero, and
• (iv) training the binary classification model using the first labeled historical narrative sentence and the second labeled historical narrative sentence from the training input;
• responsive to a determination that the binary classification is the irrelevant classification, generating a pertinent summary by removing the sentence from the narrative data object;
• and
• outputting, based at least in part on the pertinent summary, an output indicia data object for the narrative data object.