1. A computer implemented method of generating a language section from tabular data in an electronic document, the method comprising: identifying, by a processor, in a first tabular portion of the electronic document, a set of categories used to organize tabular data, wherein the set of categories includes headers, labels, and content cells; determining that the set of categories includes a content cell that contains an image by detecting an image file extension associated with the content cell; identifying, by the processor and using heuristics and machine learning, contextual relationships between the tabular data, wherein the contextual relationships are created by the set of categories and the identifying is based in part on the position of each category in the set of categories in the first tabular portion and based on the tabular data that each category modifies; identifying, by the processor, a content characteristic for each of the set of categories in the first tabular portion, wherein identifying a content characteristic for the content cell that contains an image comprises utilizing optical character recognition techniques; and generating, by the processor, a first language section from at least two distinct categories of the set of categories, wherein a format of the first language section is based on the content characteristic and contextual relationships for each of the at least two distinct categories of the set of categories and is governed by a formatting table, wherein the formatting table comprises example combinations of content characteristics for example categories of an example tabular portion and formatting rules for generating example language sections from those example combinations.
1. A computer implemented method of generating a language section from tabular data in an electronic document, the method comprising: identifying, by a processor, in a first tabular portion of the electronic document, a set of categories used to organize tabular data, wherein the set of categories includes headers, labels, and content cells; determining that the set of categories includes a content cell that contains an image by detecting an image file extension associated with the content cell; identifying, by the processor and using heuristics and machine learning, contextual relationships between the tabular data, wherein the contextual relationships are created by the set of categories and the identifying is based in part on the position of each category in the set of categories in the first tabular portion and based on the tabular data that each category modifies; identifying, by the processor, a content characteristic for each of the set of categories in the first tabular portion, wherein identifying a content characteristic for the content cell that contains an image comprises utilizing optical character recognition techniques; and generating, by the processor, a first language section from at least two distinct categories of the set of categories, wherein a format of the first language section is based on the content characteristic and contextual relationships for each of the at least two distinct categories of the set of categories and is governed by a formatting table, wherein the formatting table comprises example combinations of content characteristics for example categories of an example tabular portion and formatting rules for generating example language sections from those example combinations. 6. The method of claim 1 , further comprising: identifying, in a second tabular portion of the electronic document, a second set of categories used to organize tabular data therein; identifying the content characteristic for each category of the second set of categories in the second tabular portion; generating a second language section from at least two distinct categories of the second set of categories, the format of the second language section based on the content characteristic for each of the at least two distinct categories of the second set of categories; and merging the first language section and the second language section in response to determining that the first language section is substantially similar to the second language section.