Neural network-based document searching system

1. A system, comprising:

one or more processors; and

memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to:

receive a first document comprising textual content;

summarize the textual content using a first neural network;

translate the summarized textual content into translated content using a second neural network, the translated content being in an intermediate language;

generate n-grams for each word of the summarized textual content in the intermediate language;

conduct a fuzzy search using the generated n-grams for similar translated content from a plurality of documents, wherein the similar translated content was previously stored in a database; and

output results of the fuzzy search.

2. The system of claim 1 , further comprising storing additional instructions that when executed by the one or more processors, are configured to cause the system to store the translated content in the database.

3. The system of claim 1 , wherein the database comprises a link to each of the plurality of documents, a summary of each of the plurality of documents, and a translation of the summary of each of the plurality of documents.

4. The system of claim 3 , wherein content from the plurality of documents stored in the database has been previously summarized and translated into the intermediate language.

5. The system of claim 4 , wherein conducting the fuzzy search comprises comparing the translated content of the document to the translated content of each of the plurality of documents in the database.

6. The system of claim 1 , wherein outputting the results of the fuzzy search comprises causing a user device to display results of the fuzzy search, and wherein each of the results comprising summarized textual content of an associated document of the plurality of documents.

7. The system of claim 1 , wherein the first neural network comprises a first recurrent neural network (RNN), a first convolutional neural network (CNN), a transformer, or a combination thereof, and the intermediate language is lojban.

8. The system of claim 7 , wherein the second neural network comprises a second recurrent neural network (RNN), a second convolutional neural network (CNN), a transformer, or a combination thereof.

9. A system, comprising:

one or more processors; and

memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to:

receive a first document comprising textual content;

summarize the textual content using a first neural network;

translate the summarized textual content into translated content using a second neural network, the translated content being in an intermediate language;

conduct a fuzzy search using the translated textual content using a technique selected from Bitap, n-gram, Levenshtein Distance, BK-tree, and Soundex for similar translated content from a plurality of documents, wherein the similar translated content was previously stored in a database; and

output results of the comparison.

10. The system of claim 9 , further comprising storing additional instructions that when executed by the one or more processors, are configured to cause the system to store the translated content in the database.

11. The system of claim 9 , wherein content from the plurality of documents stored in the database has been previously summarized and translated into the intermediate language.

12. The system of claim 9 , wherein outputting the results of the comparison comprises causing a user device to display the results of the comparison.

13. The system of claim 12 , wherein each of the results comprise summarized textual content of an associated document of the plurality of documents.

14. The system of claim 9 , wherein the intermediate language is lojban.

15. The system of claim 9 , wherein the first neural network comprises a first recurrent neural network (RNN), a first convolutional neural network (CNN), a transformer, or a combination thereof.

16. The system of claim 15 , wherein the second neural network comprises a second recurrent neural network (RNN), a second convolutional neural network (CNN), a transformer, or a combination thereof, and wherein the third neural network is an autoencoder.

17. A system, comprising:

one or more processors; and

memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to:

receive text;

summarize the text using a first neural network;

translate the summarized text into translated content in an intermediate language using a second neural network;

generate n-grams for each word of the summarized text in the intermediate language;

conduct a fuzzy search using the generated n-grams for similar translated text from a plurality of documents, wherein the similar translated text was previously stored in a database; and

output results of the fuzzy search.

18. The system of claim 17 , wherein the one or more identified documents are stored in a database and content from the one or more identified documents has been previously summarized and translated into translated content in the intermediate language.

19. The system of claim 18 , wherein conducting the fuzzy search comprises comparing the translated content to the translated content of the one or more identified documents in the database.