  Long short-term memory(LSTM) units on sequence-based models are being used in
translation, question-answering systems, classification tasks due to their
capability of learning long-term dependencies. In Natural language generation,
LSTM networks are providing impressive results on text generation models by
learning language models with grammatically stable syntaxes. But the downside
is that the network does not learn about the context. The network only learns
the input-output function and generates text given a set of input words
irrespective of pragmatics. As the model is trained without any such context,
there is no semantic consistency among the generated sentences. The proposed
model is trained to generate text for a given set of input words along with a
context vector. A context vector is similar to a paragraph vector that grasps
the semantic meaning(context) of the sentence. Several methods of extracting
the context vectors are proposed in this work. While training a language model,
in addition to the input-output sequences, context vectors are also trained
along with the inputs. Due to this structure, the model learns the relation
among the input words, context vector and the target word. Given a set of
context terms, a well trained model will generate text around the provided
context. Based on the nature of computing context vectors, the model has been
tried out with two variations (word importance and word clustering). In the
word clustering method, the suitable embeddings among various domains are also
explored. The results are evaluated based on the semantic closeness of the
generated text to the given context.
