Github user rxin commented on the issue:
https://github.com/apache/spark/pull/13604
To summarize:
For people coming from RDD land, they would assume read.text to return
Dataset[String], and it is easier to program for those guys.
That said, there are also arguments for using generic DataFrame:
1. Consistent with other methods in DataFrameReader (since all other
methods return DataFrame) -- but you could argue text is the only one that has
a fixed schema, when partitioning is off.
2. Would be able to support partitioning and make it the same as
`format("text").load("...")`
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