  Comprehending meaning from natural language is a primary objective of Natural
Language Processing (NLP), and text comprehension is the cornerstone for
achieving this objective upon which all other problems like chat bots, language
translation and others can be achieved. We report a Summary-Attentive Reader we
designed to better emulate the human reading process, along with a
dictiontary-based solution regarding out-of-vocabulary (OOV) words in the data,
to generate answer based on machine comprehension of reading passages and
question from the SQuAD benchmark. Our implementation of these features with
two popular models (Match LSTM and Dynamic Coattention) was able to reach close
to matching the results obtained from humans.
