Systems and methods for abstractive document summarization with entity coverage control

A system for abstractive summarization of a document, the system comprising:

• a non-transitory memory;
• and
• one or more processor coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising:
• receiving, via a data interface, a training dataset comprising a plurality of articles and a plurality of summaries corresponding to the plurality of articles;
• generating a plurality of article-summary pairs by pairing each article with at least one associated summary;
• computing, for an article-summary pair, an entity coverage precision metric based on a number of entity mentions in a corresponding summary or a corresponding article;
• determining a pseudo label indicating a faithfulness level of the corresponding article and the corresponding summary based on the computed entity coverage precision metric;
• prepending the article with the determined pseudo label as a training input to a summarization model;
• generating, by the summarization model, an output summary conditioned on both the article and the prepended pseudo label;
• and
• updating the summarization model based on a training objective comparing the output summary and the corresponding summary.

A non-transitory, machine-readable medium having stored thereon machine-readable instructions executable to cause a system to perform operations comprising:

• receiving, via a data interface, a training dataset comprising a plurality of articles and a plurality of summaries associated with the plurality of articles;
• generating a plurality of article-summary pairs by pairing each article with at least one associated summary;
• computing, for an article-summary pair, an entity coverage precision metric based on a number of entity mentions in a corresponding summary or a corresponding article;
• determining a pseudo label indicating a faithfulness level of the corresponding article and the corresponding summary based on the computed entity coverage precision metric;
• prepending the article with the determined pseudo label as a training input to a summarization model;
• generating, by the summarization model, an output summary conditioned on both the article and the prepended pseudo label;
• and
• updating the summarization model based on a training objective comparing the output summary and the corresponding summary.