Explainable and efficient text summarization
A computer-implemented, machine learning method for generating explainable text summaries includes extracting a subset of sentences from an input document as an extractive summary and adding context to the extracted sentences to generate a prompt. A fluent summary is generated by using the prompt as input to a generative language model. Source information for a sentence from the fluent summary is determined by mapping the sentence from the fluent summary to a sentence in the extractive summary and the sentence from the extractive summary to a sentence from the input document. A transparent summary view is generated showing the sentence from the fluent summary along with the source information from the extractive summary and the input document for display on a user interface. The method has applications including, but not limited to medical AI, public safety and other machine learning applications for reliable and explainable document summarization.
1 . A computer-implemented, machine learning method for generating explainable text summaries, the method comprising:
extracting a subset of sentences from at least one input document as an extractive summary;
adding context to the extracted sentences to generate a prompt;
generating a fluent summary by using the prompt as input to a generative language model;
determining source information for a sentence from the fluent summary by mapping the sentence from the fluent summary to at least one sentence in the extractive summary and the at least one sentence from the extractive summary to at least one sentence from the at least one input document; and
generating a transparent summary view showing the sentence from the fluent summary along with the source information from the extractive summary and the at least one input document for display on a user interface,
wherein mapping the sentence from the fluent summary to the at least one sentence in the extractive summary is performed by embedding the sentence from the fluent summary and each respective one of the extracted sentences as a numerical vector using a sentence embedding model, and selecting a number k of the extracted sentences that are nearest neighbors to the sentence from the fluent summary as evidence in the extractive summary.
2 . The method according to claim 1 , wherein mapping the sentence from the fluent summary to the at least one sentence in the extractive summary and/or mapping the at least one sentence from the extractive summary to the at least one sentence from the at least one input document is performed using a natural language inference model that predicts for the mapping whether a respective one of the sentences is entailed by another one of the sentences.
3 . The method according to claim 1 , wherein mapping the at least one sentence from the extractive summary to the at least one sentence from the at least one input document is performed by embedding each respective one of the at least one sentence from the at least one input document as a numerical vector using a sentence embedding model, and selecting, as evidence in the input documents, a number k of the at least one sentence from the at least one input document that are nearest neighbors to the number k of the extracted sentences that are in the evidence in the extractive summary.
4 . The method according to claim 1 , further comprising removing meaningless words and phrases from the extracted sentences prior to generating the prompt.
5 . The method according to claim 4 , wherein the meaningless words and phrases are determined by comparing the extracted sentences to a database containing words and phrases that have been previously classified as meaningless.
6 . The method according to claim 1 , further comprising determining the subset of sentences using a neural network that receives the at least one input document and outputs an informativeness score for each sentence contained in the at least one input document.
7 . The method according to claim 1 , wherein adding the context to the extracted sentences includes resolving ambiguities in individual ones of the extracted sentences by performing co-reference resolution and entity linking based on the at least input document.
8 . The method according to claim 1 , further comprising checking whether one or more of the extracted sentences is a duplicate by semantically comparing embeddings of the extracted sentences using a similarity threshold, and excluding the one or more of the extracted sentences from the prompt based on a determination that the one or more of the extracted sentences is within the similarity threshold to another one of the extracted sentences.
9 . The method according to claim 1 , wherein the prompt comprises a list of the extracted sentences and, for respective ones of the extracted sentences having the added context, the added context is concatenated to the respective extracted sentence, and wherein the prompt further comprises an instruction to the generative language model to summarize, paraphrase or re-write the extracted sentences, which is output as the fluent summary.
10 . The method according to claim 1 , wherein the transparent summary view highlights on the user interface the sentence from the fluent summary as well as the source information including the at least one sentence from the extractive summary and the at least one sentence from the at least one input document.
11 . The method according to claim 1 , wherein the at least one input document includes patient data, and wherein the transparent summary view is used to support decision-making in a medical Artificial Intelligence (AI) or automated healthcare use case.
12 . The method according to claim 1 , wherein the at least one input document includes a criminal investigation report, and wherein the transparent summary view is used to support decision-making in a public safety use case and/or to activate a forensic tool.
13 . A computer system for generating text summaries comprising one or more processors which, alone or in combination, are configured to perform a machine learning method for generating explainable text summaries comprising the following steps:
extracting a subset of sentences from at least one input document as an extractive summary;
adding context to the extracted sentences to generate a prompt;
generating a fluent summary by using the prompt as input to a generative language model;
determining source information for a sentence from the fluent summary by mapping the sentence from the fluent summary to at least one sentence in the extractive summary and the at least one sentence from the extractive summary to at least one sentence from the at least one input document; and
generating a transparent summary view showing the sentence from the fluent summary along with the source information from the extractive summary and the at least one input document for display on a user interface,
wherein mapping the sentence from the fluent summary to the at least one sentence in the extractive summary is performed by embedding the sentence from the fluent summary and each respective one of the extracted sentences as a numerical vector using a sentence embedding model, and selecting a number k of the extracted sentences that are nearest neighbors to the sentence from the fluent summary as evidence in the extractive summary.
14 . A tangible, non-transitory computer-readable medium for generating explainable text summaries containing instructions which, upon being executed by one or more hardware processors, provide for execution of a machine learning method comprising the following steps:
extracting a subset of sentences from at least one input document as an extractive summary;
adding context to the extracted sentences to generate a prompt;
generating a fluent summary by using the prompt as input to a generative language model;
determining source information for a sentence from the fluent summary by mapping the sentence from the fluent summary to at least one sentence in the extractive summary and the at least one sentence from the extractive summary to at least one sentence from the at least one input document; and
generating a transparent summary view showing the sentence from the fluent summary along with the source information from the extractive summary and the at least one input document for display on a user interface,
wherein mapping the sentence from the fluent summary to the at least one sentence in the extractive summary is performed by embedding the sentence from the fluent summary and each respective one of the extracted sentences as a numerical vector using a sentence embedding model, and selecting a number k of the extracted sentences that are nearest neighbors to the sentence from the fluent summary as evidence in the extractive summary.