Systems and methods for explainable and factual multi-document summarization

A system for training a multi-document summarization model, the system comprising:

• a memory that stores the multi-document summarization model;
• a communication interface that receives a plurality of documents and a reference summary associated with the plurality of documents;
• and
• one or more hardware processors that:
• generates embeddings of sentences from the plurality of documents, wherein the embeddings indicate a relationship between the sentences across the plurality of documents;
• clusters, based on the embeddings, the sentences from the plurality of documents into a plurality of clusters;
• aligns one or more reference sentences in the reference summary with the plurality of clusters into a plurality of aligned reference sentence clusters, respectively;
• masks a first sentence from one of the plurality of documents based on a determination that the first sentence is contradicted by a second sentence of the plurality of documents
• generates, by a natural language processing model without using the first sentence based on the masking, a plurality of cluster-wise summaries corresponding to the plurality of clusters, respectively;
• compares the plurality of cluster-wise summaries and the plurality of aligned reference sentence clusters to compute a loss;
• and
• updates the natural language processing model based on the loss.

A processor-readable non-transitory storage medium storing a plurality of processor-executable instructions for training a multi-document summarization model, the instructions being executed by a processor to perform operations comprising:

• receiving, via a communication interface, a plurality of documents and a reference summary associated with the plurality of documents;
• generating embeddings of sentences from the plurality of documents, wherein the embeddings indicate a relationship between the sentences across the plurality of documents;
• clustering, based on the embeddings, the sentences from the plurality of documents into a plurality of clusters;
• aligning one or more reference sentences in the reference summary with the plurality of clusters into a plurality of aligned reference sentence clusters, respectively;
• masking a first sentence from one of the plurality of documents based on a determination that the first sentence is contradicted by a second sentence of the plurality of documents
• generating, by a natural language processing model without using the first sentence based on the masking, a plurality of cluster-wise summaries corresponding to the plurality of clusters, respectively;
• comparing the plurality of cluster-wise summaries and the plurality of aligned reference sentence clusters to compute a loss;
• and
• updating the natural language processing model based on the loss.