Systems and methods for scienetific contribution summarization

A system for training a natural language processing (NLP) classifier, comprising:

• a non-transitory memory;
• and
• one or more processors 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 a portion of the nonfiction article, wherein the portion comprises a plurality of text segments and wherein the portion is associated with citation information including:
• (i) an inbound citation that post-dates the nonfiction article, (ii) an outbound citation that pre-dates the nonfiction article, or (iii) any combination thereof;
• inputting the portion of the nonfiction article and the citation information associated with the portion to a natural language processing (NLP) model;
• for a text segment of the plurality of text segments in the portion:
• (i) determining, by the NPL model, that a first part of the text segment is background information by detecting an outbound citation associated with the text segment;
• (ii) determining, by the NPL model, that a second part of the text segment is a new contribution attributable to the nonfiction article by detecting an inbound citation associated with the text segment, or
• (iii) any combination thereof;
• computing a loss objective based at least in part on:
• (i) a first conditional probability distribution of the background information conditioned on given information of the outbound citation;
• (ii) a second conditional probability distribution of the new contribution conditioned on given information of the inbound citation;
• or (iii) any combination thereof;
• and
• updating the NLP model using the loss objective via backpropagation.

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

• receiving a portion of the nonfiction article, wherein the portion comprises a plurality of text segments and wherein the portion is associated with citation information including:
• (i) an inbound citation that post-dates the nonfiction article, (ii) an outbound citation that pre-dates the nonfiction article, or (iii) any combination thereof;
• inputting the portion of the nonfiction article and the citation information associated with the portion to a natural language processing (NLP) model;
• for a text segment of the plurality of text segments in the portion:
• (i) determining, by the NPL model, that a first part of the text segment is background information by detecting an outbound citation associated with the text segment;
• (ii) determining, by the NPL model, that a second part of the text segment is a new contribution attributable to the nonfiction article by detecting an inbound citation associated with the text segment, or
• (iii) any combination thereof;
• computing a loss objective based at least in part on:
• (i) a first conditional probability distribution of the background information conditioned on given information of the outbound citation;
• (ii) a second conditional probability distribution of the new contribution conditioned on given information of the inbound citation;
• or (iii) any combination thereof;
• and
• updating the NLP model using the loss objective via backpropagation.