1. Build and evaluate Word2Vec Models using different Text corpora.
2. Use the best model to implement Word Mover Distance.
3. Optimize the Word Mover Distance using three techniques proposed by Kusner et al. a. Word Centroid Distance b. Relaxed Word Mover Distance
   c. Prefetch and Prune
4. Use Word Mover Distance to explore different heuristics to generate summaries.

Reference: Distributed Representations of Words and Phrases and their Compositionality: Thomas Mikoloc et al. From Word Embeddings To Document Distances: Matt J kusner

Note: First we need to build the word2vec model to generate word vectors. These are used to generate document distances and summaries.