Natural language generation using knowledge graph incorporating textual summaries

A non-transitory computer readable medium including instructions that, when executed by one or more processors, cause the one or more processors to perform a set of actions for producing an answer to a question relating to a domain, the set of actions including:

• receiving a natural-language textual sequence representing the question;
• retrieving, from a knowledge graph associated with the domain, a first textual passage and a second textual passage based on rankings with respect to the natural-language textual sequence, a first textual summary summarizing textual information in a first vicinity of the first textual passage, and a second textual summary summarizing textual information in a second vicinity of the second textual passage, wherein the knowledge graph includes a tree corresponding to a document of the textual information, a root of the tree corresponding to a summary of the document, nodes of the tree corresponding to textual summaries of portions of the document, and leaves of the tree corresponding to the textual passages of the document, and wherein the knowledge graph is generated using:
• at least a portion of textual information including a plurality of documents relevant to the domain provided to a language model, the textual information is modified into smaller-sized portions using a semantic chunking algorithm to produce modified textual information of a size less than a context window of the language model based on a determination that the textual information exceeds the context window of the language model, the context window corresponds to a limited input size of the language model, the smaller-sized portions of the textual information are tokens,
• the tokens are converted into high dimensional input sequence of vectors where an input sequence of vector numerically represents the tokens,
• the language model includes one or more layers of encoders and decoders to process the high dimensional input sequence of vectors to generate an output sequence, the output sequence is used to determine probabilities of successive output tokens which are used to determine an output of the language model, and
• the language model trained to produce the output of textual passages, descriptions of associations between the textual passages, and textual summaries corresponding to the textual passages using the modified textual information;
• obtaining the answer to the question using the language model by encoding a first intermediate output based on the natural-language textual sequence, the first textual passage, and the first textual summary, encoding a second intermediate output based on the natural-language textual sequence, the second textual passage, and the second textual summary, and decoding a concatenation of the first intermediate output and the second intermediate output;
• and
• providing the output to a user on a user interface, based on the answer.

A system including at least one processor and at least one computer readable medium including instructions that, when executed by the at least one processor, cause the at least one processor to perform a set of actions for producing an answer to a question relating to a domain, the set of actions including:

• receiving a natural-language textual sequence representing the question;
• retrieving, from a knowledge graph associated with the domain, a first textual passage and a second textual passage based on rankings with respect to the natural-language textual sequence, a first textual summary summarizing textual information in a first vicinity of the first textual passage, and a second textual summary summarizing textual information in a second vicinity of the second textual passage, wherein the knowledge graph includes a tree corresponding to a document of the textual information, a root of the tree corresponding to a summary of the document, nodes of the tree corresponding to textual summaries of portions of the document, and leaves of the tree corresponding to the textual passages of the document, and wherein the knowledge graph is generated using:
• at least a portion of textual information including a plurality of documents relevant to the domain provided to a language model, the textual information is modified into smaller-sized portions using a semantic chunking algorithm to produce modified textual information of a size less than a context window of the language model based on a determination that the textual information exceeds the context window of the language model, the context window corresponds to a limited input size of the language model, the smaller-sized portions of the textual information are tokens,
• the tokens are converted into high dimensional input sequence of vectors where an input sequence f vector numerically represents the tokens,
• the language model includes one or more layers of encoders and decoders to process the high dimensional input sequence of vectors to generate an output sequence, the output sequence is used to determine probabilities of successive output tokens which are used to determine an output of the language model, and
• the language model trained to produce the output of textual passages, descriptions of associations between the textual passages, and textual summaries corresponding to the textual passages using the modified textual information;
• obtaining the answer to the question using the language model by encoding a first intermediate output based on the natural-language textual sequence, the first textual passage, and the first textual summary, encoding a second intermediate output based on the natural-language textual sequence, the second textual passage, and the second textual summary, and decoding a concatenation of the first intermediate output and the second intermediate output;
• and
• providing the output to a user based on a user interface, the answer.