System and method for machine assisted documentation in medical writing

A system (100) for a machine assisted documentation in a medical writing, comprising:

• a memory (102) storing instructions;
• one or more communication interfaces (106);
• and
• one or more hardware processors (104) coupled to the memory (102) via the one or more communication interfaces (106), wherein the one or more hardware processors (104) are configured by the instructions to:
• receive, a plurality of input documents as an input for different type of documents to be created;
• process, the inputted plurality of documents by extracting into an at least one section to generate a list of sections, wherein the sections are processed based on a natural language processing;
• classify, at least one category from a plurality of categories corresponding to the at least one section to generate a summary set, wherein the at least one category corresponds to a Summarizable category, wherein the summary set comprises a plurality of sentences;
• generate, at least one of (i) a local context, (ii) a global context, and combination thereof based on the summary set, wherein a knowledge base is utilized for the local context and the global context for processing a plurality of natural language inputs;
• parse, at least one sentence based on the generated local context, or global context to generate at least one sequence of the plurality of sentences, wherein sequencing of the plurality of documents and the sections is pre-defined;
• process, the at least one sequence of the plurality of sentences to generate a queue of the at least one sequence;
• and
• validate, the queue of the at least one sequence to obtain a combined summary set, wherein validation is based on abstractive summarization, extractive summarization, feature extraction and Table, Figures and Listings (TFL) summarization to extract output for respective document and generate the summary, wherein generating the summary includes at least one of (i) a context generator, (ii) a section content parser, and (iii) a logical sequence tagger, wherein the context generator is configured to create the local context of input data, wherein the context generator is configured to generate a context set for use in summarization and the feature extraction, wherein the logical sequence tagger is configured to create tagging information for maintaining the sequence of plurality of input document sequences for maintaining logical sequence and wherein the generated summary is validated and updated by generating a final output.

One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors perform actions comprising:

• receiving, a plurality of input documents as an input for different type of documents to be created;
• processing, the inputted plurality of documents by extracting into an at least one section to generate a list of sections, wherein the sections are processed based on a natural language processing;
• classifying, at least one category from a plurality of categories corresponding to the at least one section to generate a summary set, wherein the at least one category corresponds to a Summarizable category, wherein the summary set comprises a plurality of sentences;
• generating, at least one of (i) a local context, (ii) a global context, and combination thereof based on the summary set, wherein a knowledge base is utilized for the local context and the global context for processing a plurality of natural language inputs;
• parsing, at least one sentence based on the generated local context, or global context to generate at least one sequence of the plurality of sentences, wherein sequencing of the plurality of documents and the sections is pre-defined;
• processing, the at least one sequence of the plurality of sentences to generate a queue of the at least one sequence;
• and
• validating, the queue of the at least one sequence to obtain a combined summary set, wherein validation is based on abstractive summarization, extractive summarization, feature extraction and Table, Figures and Listings (TFL) summarization to extract output for respective document and generate the summary, wherein generating the summary includes at least one of (i) a context generator, (ii) a section content parser, and (iii) a logical sequence tagger, wherein the context generator is configured to create the local context of input data;
• generating a context set for use in summarization and the feature extraction;
• creating tagging information for maintaining the sequence of plurality of input document sequences for maintaining logical sequence and wherein the generated summary is validated and updated by generating a final output.