User interface for use with a search engine for searching financial related documents
A method for rendering context based information on a user interface includes receiving a user request to extract the context based information from a database. The database includes a plurality of documents and the request includes at least one search criteria required to determine a context of the user request. The method includes generating a list of documents corresponding to the context of the user request and rendering on a viewing portion of the user interface the list of documents corresponding to the context of the user request.
1 . A system for generating a plurality of summaries from a plurality of documents onto a user interface of a remote computer, the system comprising:
a memory storing instructions to be executed by one or more hardware processors; and
one or more hardware processors configured to execute the instructions stored in the memory, wherein the instructions, when executed by the one or more hardware processors, cause the system to:
receive a user query from the remote computer to search for information from the plurality of documents, wherein the user query is based on input from a user at the remote computer;
execute a search of one or more of the plurality of documents based on the user query;
generate search results in response to executing the search,
wherein the search results comprise relevant documents from the plurality of documents, and
wherein the relevant documents are determined to be relevant based on one or more identified topics associated with the user query;
cause a response to the user query to be rendered on the user interface of the remote computer,
wherein the response to the user query is based on two or more of the relevant documents from the search results,
wherein the response to the user query includes multiple portions of content, with each portion of content including textual data based on at least one of the two or more relevant documents, and
wherein each portion of content is generated using a neural network model applied to the at least one of the two or more relevant documents that the respective portion of content is based on;
receive a selection from the user identifying a selected document associated with one of the multiple portions of content,
wherein the selected document is one of the at least one of the two or more relevant documents that the portion of content is based on; and
cause at least a portion of the selected document from the search results to be rendered on the user interface of the remote computer.
2 . The system of claim 1 , wherein the instructions, when executed by the one or more hardware processors, further cause the system to use a machine-learning model to determine at least one keyword associated with the user query.
3 . The system of claim 2 , wherein the search is executed based on the at least one keyword associated with the user query.
4 . The system of claim 2 , wherein the relevant documents are determined to be relevant based on the at least one keyword associated with the user query.
5 . The system of claim 2 , wherein, as part of the search of the one or more of the plurality of documents based on the user query, a snippet of text is identified in one or more of the plurality of documents being searched based on the at least one keyword appearing in said document being searched.
6 . The system of claim 2 , wherein, as part of the search of the one or more of the plurality of documents based on the user query, a snippet of text is identified in one or more of the plurality of documents being searched based on multiple keywords from the at least one keyword appearing in the same sentence in said document being searched.
7 . The system of claim 2 , wherein, as part of the search of the one or more of the plurality of documents based on the user query, a snippet of text is identified in one or more of the plurality of documents being searched based on multiple keywords from the at least one keyword appearing in proximity to one another in said document being searched.
8 . The system of claim 1 , wherein:
each portion of content includes textual data based on multiple of the relevant documents,
each portion of content is generated using the neural network model applied to the multiple relevant documents that the respective portion of content is based on, and
the selected document is one of the multiple relevant documents that the portion of content is based on.
9 . The system of claim 1 , wherein:
the rendered portion of the selected document includes an identified snippet of text from the selected document, and
the identified snippet of text is displayed differently in the rendered portion of the selected document.
10 . The system of claim 1 , wherein the instructions, when executed by the one or more hardware processors, further cause the system to determine at least one similar topic based on the user query, wherein the at least one similar topic is related to the one or more identified topics associated with the user query.
11 . The system of claim 10 , wherein the relevant documents are determined to be relevant further based on the at least one similar topic.
12 . The system of claim 10 , wherein:
the rendered portion of the selected document includes an identified snippet of text from the selected document, and
the identified snippet of text is identified based on the at least one similar topic.
13 . The system of claim 10 , wherein each portion of content is associated with one or more of the at least one similar topic.
14 . The system of claim 10 , wherein:
a first portion of content of the multiple portions of content is associated with a first similar topic,
a second portion of content of the multiple portions of content is associated with a second similar topic, and
the first similar topic and the second similar topic are based on the user query.
15 . The system of claim 1 , wherein:
a first portion of content of the multiple portions of content is associated with a first keyword,
a second portion of content of the multiple portions of content is associated with a second keyword, and
the first keyword and the second keyword are based on the user query.
16 . A computer-implemented method for generating a plurality of summaries from a plurality of documents onto a user interface of a remote computer, the computer-implemented method comprising:
receiving a user query from the remote computer to search for information from the plurality of documents, wherein the user query is based on input from a user at the remote computer;
executing a search of one or more of the plurality of documents based on the user query;
generating search results in response to executing the search,
wherein the search results comprise relevant documents from the plurality of documents, and
wherein the relevant documents are determined to be relevant based on one or more identified topics associated with the user query;
causing a response to the user query to be rendered on the user interface of the remote computer,
wherein the response to the user query is based on two or more of the relevant documents from the search results,
wherein the response to the user query includes multiple portions of content, with each portion of content including textual data based on at least one of the two or more relevant documents, and
wherein each portion of content is generated using a neural network model applied to the at least one of the two or more relevant documents that the respective portion of content is based on;
receiving a selection from the user identifying a selected document associated with one of the multiple portions of content,
wherein the selected document is one of the at least one of the two or more relevant documents that the portion of content is based on; and
causing at least a portion of the selected document from the search results to be rendered on the user interface of the remote computer.
17 . The computer-implemented method of claim 16 , further comprising using a machine-learning model to determine at least one keyword associated with the user query.
18 . The computer-implemented method of claim 17 , wherein the search is executed based on the at least one keyword associated with the user query.
19 . The computer-implemented method of claim 17 , wherein the relevant documents are determined to be relevant based on the at least one keyword associated with the user query.
20 . The computer-implemented method of claim 17 , wherein, as part of the search of the one or more of the plurality of documents based on the user query, a snippet of text is identified in one or more of the plurality of documents being searched based on the at least one keyword appearing in said document being searched.
21 . The computer-implemented method of claim 17 , wherein, as part of the search of the one or more of the plurality of documents based on the user query, a snippet of text is identified in one or more of the plurality of documents being searched based on multiple keywords from the at least one keyword appearing in the same sentence in said document being searched.
22 . The computer-implemented method of claim 17 , wherein, as part of the search of the one or more of the plurality of documents based on the user query, a snippet of text is identified in one or more of the plurality of documents being searched based on multiple keywords from the at least one keyword appearing in proximity to one another in said document being searched.
23 . The computer-implemented method of claim 16 , wherein:
each portion of content includes textual data based on multiple of the relevant documents,
each portion of content is generated using the neural network model applied to the multiple relevant documents that the respective portion of content is based on, and
the selected document is one of the multiple relevant documents that the portion of content is based on.
24 . The computer-implemented method of claim 16 , wherein:
the rendered portion of the selected document includes an identified snippet of text from the selected document, and
the identified snippet of text is displayed differently in the rendered portion of the selected document.
25 . The computer-implemented method of claim 16 , further comprising determining at least one similar topic based on the user query, wherein the at least one similar topic is related to the one or more identified topics associated with the user query.
26 . The computer-implemented method of claim 25 , wherein the relevant documents are determined to be relevant further based on the at least one similar topic.
27 . The computer-implemented method of claim 25 , wherein:
the rendered portion of the selected document includes an identified snippet of text from the selected document, and
the identified snippet of text is identified based on the at least one similar topic.
28 . The computer-implemented method of claim 25 , wherein each portion of content is associated with one or more of the at least one similar topic.
29 . The computer-implemented method of claim 25 , wherein:
a first portion of content of the multiple portions of content is associated with a first similar topic,
a second portion of content of the multiple portions of content is associated with a second similar topic, and
the first similar topic and the second similar topic are based on the user query.
30 . The computer-implemented method of claim 16 , wherein:
a first portion of content of the multiple portions of content is associated with a first keyword,
a second portion of content of the multiple portions of content is associated with a second keyword, and
the first keyword and the second keyword are based on the user query.