Automated document highlighting in a digital management platform

A non-transitory computer readable storage medium comprising computer executable instructions for automatically highlighting text passages within a target document that when executed by one or more processors causes the one or more processors to perform operations comprising:

• accessing a training set of data comprising documents each with a set of highlighted text passages;
• training a machine learned model using the training set of data, the machine learned model configured to identify a set of text passages of a document to highlight;
• identifying a first candidate set of text passages of the target document by:
• converting text of the target document into word vectors,
• generating a matrix representation of the target document, each row of the matrix representation corresponding to a text passage of the target document, the values of each row corresponding to the word vectors,
• identifying a threshold portion of the text passages of the target document that are most relevant to the target document using an unweighted graph generated based on the generated matrix representation;
• identifying a second candidate set of text passages within the target document based on characteristics of text of the second candidate set of text passages;
• identifying a third candidate set of text passages within the target document based on feedback from a user;
• identifying a target set of text passages to highlight by applying the machine learned model to the first candidate set of text passages, the second candidate set of text passages, and the third candidate set of text passages;
• and
• modifying the target document by highlighting the target set of text passages.

A computer system for automatically highlighting text passages within a target document comprising:

• one or more computer processors;
• and
• a non-transitory computer readable storage medium comprising computer executable instructions that when executed by one or more processors causes the one or more processors to perform operations comprising:
• accessing a training set of data comprising documents each with a set of highlighted text passages;
• training a machine learned model using the training set of data, the machine learned model configured to identify a set of text passages of a document to highlight;
• identifying a first candidate set of text passages of the target document by:
• converting text of the target document into word vectors,
• generating a matrix representation of the target document, each row of the matrix representation corresponding to a text passage of the target document, the values of each row corresponding to the word vectors, and
• identifying a threshold portion of the text passages of the target document that are most relevant to the target document using an unweighted graph generated based on the generated matrix representation;
• identifying a second candidate set of text passages within the target document based on characteristics of text of the second candidate set of text passages;
• identifying a third candidate set of text passages within the target document based on feedback from a user;
• identifying a target set of text passages to highlight by applying the machine learned model to the first candidate set of text passages, the second candidate set of text passages, and the third candidate set of text passages;
• and
• modifying the target document by highlighting the target set of text passages.

A method of automatically highlighting text passages within a target document comprising:

• identifying a first candidate set of text passages of the target document by:
• converting text of the target document into word vectors,
• generating a matrix representation of the target document, each row of the matrix representation corresponding to a text passage of the target document, the values of each row corresponding to the word vectors, and
• identifying a threshold portion of the text passages of the target document that are most relevant to the target document using an unweighted graph generated based on the generated matrix representation;
• identifying a second candidate set of text passages within the target document based on characteristics of text of the second candidate set of text passages;
• identifying a third candidate set of text passages within the target document based on feedback from a user;
• applying a machine learned model trained on training documents with pre-highlighted passages to the first candidate set of text passages, the second candidate set of text passages, and the third candidate set of text passages to identify a target set of text passages to highlight;
• and
• modifying the target document by highlighting the target set of text passages.