A long-term memory network method and system for text comprehension. A recurrent neural network can be provided, which includes an external memory module and a long-short term memory unit, wherein said recurrent neural network encodes raw text information into vector representations, forms memories, finds relevant sentences to answer questions, and generates multi-word answers to said questions utilizing the long short term memory unit.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A long-term memory network method for text comprehension, said method comprising: providing a recurrent neural network that includes an external memory and a long-short term memory unit, wherein said recurrent neural network encodes raw text information into vector representations, forms memories, finds relevant sentences to answer questions, and generates multi-word answers to said questions utilizing said long-short term memory unit. 2. The method of claim 1 wherein said raw text information comprises input sentences and questions. 3. The method of claim 1 wherein said recurrent neural network comprises a weakly supervised architecture. 4. The method of claim 1 wherein said recurrent neural network is trained in an end-to-end manner. 5. The method of claim 1 wherein said recurrent neural network further comprises a memory module, an answer module, a question module, and an input module. 6. The method of claim 5 wherein said answer module includes said long-short term memory unit. 7. The method of claim 1 wherein said vector representations include a matching probability vector. 8. A long-term memory system for text comprehension, said system comprising: providing a recurrent neural network that comprises an external memory and a long-short term memory unit, wherein said recurrent neural network encodes raw text information into vector representations, forms memories, finds relevant sentences to answer questions, and generates multi-word answers to said questions utilizing said long-short term memory unit. 9. The system of claim 8 wherein said raw text information comprises input sentences and questions. 10. The system of claim 8 wherein said recurrent neural network comprises a weakly supervised architecture. 11. The system of claim 8 wherein said recurrent neural network is trained in an end-to-end manner. 12. The system of claim 8 wherein said recurrent neural network further comprises a memory, an answer unit, a question unit, and an input unit. 13. The system of claim 12 wherein said answer unit includes said long-short term memory unit. 14. The system of claim 8 wherein said vector representations include a matching probability vector. 15. A long-term memory network system for text comprehension, said system comprising: at least one processor; and a computer-usable medium embodying computer program code, said computer-usable medium capable of communicating with said at least one processor, said computer program code comprising instructions executable by said at least one processor and configured for: configuring a recurrent neural network with an external memory and long-short term memory unit; and instructing said recurrent neural network to encode raw text information into vector representations, form memories, find relevant sentences to answer questions, and generate multi-word answers to said questions utilizing said long short term memory unit. 16. The system of claim 15 wherein said raw text information comprises input sentences and questions. 17. The system of claim 15 wherein said recurrent neural network comprises a weakly supervised architecture. 18. The system of claim 15 wherein said recurrent neural network is trained in an end-to-end manner. 19. The system of claim 15 wherein said recurrent neural network further comprises an answer unit, a question unit, and an input and wherein said answer unit includes said long-short term memory unit. 20. The system of claim 15 wherein said vector representations include a matching probability vector.