System and method for machine assisted documentation in medical writing

1. A processor implemented method of a machine assisted documentation in a medical writing, comprising:

receiving, via one or more hardware processors, a plurality of input documents as an input for different type of documents to be created;

processing, via the one or more hardware processors, 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, via the one or more hardware processors, 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, via the one or more hardware processors, 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, via the one or more hardware processors, 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, via the one or more hardware processors, the at least one sequence of the plurality of sentences to generate a queue of the at least one sequence; and

validating, via the one or more hardware processors, 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.

2. The processor implemented method of claim 1 , wherein the plurality of logical categories corresponds to at least one of (i) a Summarizable sections, (ii) brief summaries, and (iii) text sections excluded.

3. The processor implemented method of claim 1 , wherein the Summarizable Sections category comprises at least one section of (i) introduction, (ii) problem statement, (iii) research methodology, and (iv) experimentation with associated results.

4. The processor implemented method of claim 1 , wherein the brief summaries category comprises at least one section of (i) abstract, and (ii) conclusion, wherein the text sections excluded category comprises at least one section of (i) historical background, (ii) references, and (iii) appendix.

5. The processor implemented method of claim 1 , wherein the local context comprises at least one of (i) overall context of the input documents, (ii) an extracted context of a plurality of referenced documents or combination thereof.

6. 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.

7. The system of claim 6 , wherein the plurality of logical categories corresponds to at least one of (i) a Summarizable sections, (ii) brief summaries, and (iii) Text sections excluded.

8. The system of claim 6 , wherein the Summarizable Sections category comprises at least one section of (i) introduction, (ii) problem statement, (iii) research methodology, and (iv) experimentation with associated results.

9. The system of claim 6 , wherein the brief summaries category comprises at least one section of (i) abstract, and (ii) conclusion, wherein the text sections excluded category comprises at least one section of (i) historical background, (ii) references, and (iii) appendix.

10. The system of claim 6 , wherein the local context comprises at least one of (i) overall context of the input documents, (ii) an extracted context of a plurality of referenced documents or combination thereof.

11. 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.

12. The one or more non-transitory machine-readable information storage mediums of claim 11 , wherein the plurality of logical categories corresponds to at least one of (i) a Summarizable sections, (ii) brief summaries, and (iii) text sections excluded.

13. The one or more non-transitory machine-readable information storage mediums of claim 11 , wherein the Summarizable Sections category comprises at least one section of (i) introduction, (ii) problem statement, (iii) research methodology, and (iv) experimentation with associated results.

14. The one or more non-transitory machine-readable information storage mediums of claim 11 , wherein the brief summaries category comprises at least one section of (i) abstract, and (ii) conclusion, wherein the text sections excluded category comprises at least one section of (i) historical background, (ii) references, and (iii) appendix.

15. The one or more non-transitory machine-readable information storage mediums of claim 11 , wherein the local context comprises at least one of (i) overall context of the input documents, (ii) an extracted context of a plurality of referenced documents or combination thereof.