Systems and methods for quantum computing-based extractive summarization

Patent metadata

Field	Value
Publication number	US-2023214581-A1
Application number	US-202217647311-A
Country	US
Kind code	A1
Filing date	Jan 6, 2022
Priority date	Jan 6, 2022
Publication date	Jul 6, 2023
Grant date	—

Abstract

Systems and methods for quantum computing-based summarization are disclosed. A method for quantum computing-based summarization may include a classical computer program: receiving a document having a plurality of sentences; receiving a summary parameter that represents a subset of the plurality of sentences to include in a summary of the document; generating a vector for each sentence; calculating a centrality value for each vector; calculating a similarity value to other vectors for each vector; creating a cost function using the similarity values, the centrality values, a number of the plurality of sentences in the document, and the summary parameter; instructing a quantum computer to optimize the cost function using a quantum algorithm; receiving a dictionary comprising a plurality of distributions of the plurality of sentences and a probability for each distribution; and generating a summary comprising a subset of the plurality sentences based on a distribution having a highest probability.

First claim

1 . A method for quantum computing-based extractive summarization, comprising:
receiving, by a classical computer program, a document having a plurality of sentences; receiving, by the classical computer program, a summary parameter that represents a subset of the plurality of sentences to include in a summary of the document; generating, by the classical computer program and for each of the plurality of sentences, a vector; calculating, by the classical computer program and for each of the plurality of vectors, a centrality value; calculating, by the classical computer program and for each of the plurality of vectors, a similarity value to other vectors; receiving, by the classical computer program, a parameter gamma, and a parameter lambda, wherein the parameter gamma enforces the summary parameter, and the parameter lambda that balances the centrality value and the similarity value for the plurality of vectors; creating, by the classical computer program, a cost function using the similarity values, the centrality values, a number of the plurality of sentences in the document, the summary parameter. the parameter gamma and the parameter lambda; instructing, by the classical computer program, a quantum computer to optimize the cost function using a quantum algorithm; receiving, by the classical computer program and from the quantum computer, a dictionary comprising a plurality of distributions of the plurality of sentences and a probability for each distribution; and generating, by the classical computer program, a summary comprising a subset of the plurality sentences based on a distribution having a highest probability.

2 . (canceled)

3 . The method of claim 1 , wherein the quantum algorithm optimizes the cost function by maximizing the centrality value of the vectors while minimizing the similarity values between the vectors.

4 . The method of claim 1 , wherein the quantum algorithm comprises a Quantum Approximate Optimization Algorithm, and further comprising:
creating, by the classical computer program, a quantum circuit for the cost function, wherein the quantum circuit is an input to the Quantum Approximate Optimization Algorithm.

5 . The method of claim 1 , wherein the quantum algorithm is a quantum annealing algorithm, and the cost function is an input to the quantum annealing algorithm.

6 . The method of claim 1 , wherein the classical computer program instructs a first quantum computer to optimize the cost function using a first quantum algorithm, and a second quantum computer to optimize the cost function using a second quantum algorithm.

7 . A system, comprising:
a classical computer comprising a memory storing a classical computer program and a computer processor; and a first quantum computer in communication with the classical computer; wherein the classical computer program receives a document having a plurality of sentences; receives a summary parameter that represents a subset of the plurality of sentences to include in a summary of the document; generates, for each of the plurality of sentences, a vector; calculates, for each of the plurality of vectors, a centrality value; calculates, for each of the plurality of vectors, a similarity value to other vectors; receives a parameter gamma, and a parameter lambda, wherein the parameter gamma enforces the summary parameter, and the parameter lambda that balances the centrality value and the similarity value for the plurality of vectors; creates a cost function using the similarity values, the centrality values, a number of the plurality of sentences in the document, the summary parameter. the parameter gamma and the parameter lambda; instructs the first quantum computer to optimize the cost function using a quantum algorithm; receives, from the quantum computer, a dictionary comprising a plurality of distributions of the plurality of sentences and a probability for each distribution; and generates a summary comprising a subset of the plurality sentences based on a distribution having a highest probability; and wherein the first quantum computer optimizes the cost function using the quantum algorithm and returns the dictionary.

8 . (canceled)

9 . The system of claim 7 , wherein the quantum algorithm optimizes the cost function by maximizing the centrality value of the vectors while minimizing the similarity values between the vectors.

10 . The system of claim 7 , wherein the first quantum computer is a universal quantum computer, and the quantum algorithm comprises a Quantum Approximate Optimization Algorithm, and the classical computer program creates a quantum circuit for the cost function, wherein the quantum circuit is an input to the Quantum Approximate Optimization Algorithm.

11 . The system of claim 7 , wherein the first quantum computer comprises quantum annealing hardware, the quantum algorithm is a quantum annealing algorithm, and the cost function is an input to the quantum annealing algorithm.

12 . The system of claim 7 , further comprising a second quantum computer, and the classical computer program instructs the first quantum computer to optimize the cost function using a first quantum algorithm, and a second quantum computer to optimize the cost function using a second quantum algorithm.

13 . The system of claim 12 , wherein the first quantum computer comprises a universal quantum computer, the first quantum algorithm comprises a Quantum Approximate Optimization Algorithm, the second quantum computer comprises quantum annealing hardware, and the second quantum algorithm comprises a quantum annealing algorithm.

14 . An electronic device, comprising:
a memory storing a classical computer program; and a computer processor; wherein, when executed by the computer processor, the classical computer program causes the computer processor to:
receive a document having a plurality of sentences;
receive a summary parameter that represents a subset of the plurality of sentences to include in a summary of the document;
generate, for each of the plurality of sentences, a vector;
calculate, for each of the plurality of vectors, a centrality value;
calculates, for each of the plurality of vectors, a similarity value to other vectors;
receive a parameter gamma, and a parameter lambda, wherein the parameter gamma enforces the summary parameter, and the parameter lambda that balances the centrality value and the similarity value for the plurality of vectors;
create a cost function using the similarity values, the centrality values, a number of the plurality of sentences in the document, the summary parameter. the parameter gamma and the parameter lambda;
instruct a quantum computer to optimize the cost function using a quantum algorithm;
receive, from the quantum computer, a dictionary comprising a plurality of distributions of the plurality of sentences and a probability for each distribution; and
generate a summary comprising a subset of the plurality sentences based on a distribution having a highest probability.

15 . (canceled)

16 . The electronic device of claim 14 , wherein the quantum algorithm optimizes the cost function by maximizing the centrality value of the vectors while minimizing the similarity values between the vectors.

17 . The electronic device of claim 14 , wherein the quantum algorithm comprises a Quantum Approximate Optimization Algorithm, and the classical computer causes the computer processor to create a quantum circuit for the cost function, wherein the quantum circuit is an input to the Quantum Approximate Optimization Algorithm.

Assignees

• Jpmorgan Chase Bank Na

Inventors

• Niroula Pradeep
• Yalovetzky Romina
• Minssen Pierre
• Herman Dylan
• Pistoia Marco
• Hu Shaohan

Classifications

• G06F40/242

  Dictionaries · CPC title

• G06N10/20

  Models of quantum computing, e.g. quantum circuits or universal quantum computers · CPC title

• G06N10/60

  Quantum algorithms, e.g. based on quantum optimisation, quantum Fourier or Hadamard transforms · CPC title

• G06F40/166 Primary

  Editing, e.g. inserting or deleting · CPC title

• G06F40/30 Primary

  Semantic analysis · CPC title