Task: Summarization
Input (English): Distributional Semantics Models(DSM) derive word space from linguistic items
 in context. Meaning is obtained by defining a distance measure between vectors
 corresponding to lexical entities. Such vectors present several problems. This
 work concentrates on quality of word embeddings, improvement of word embedding
 vectors, applicability of a novel similarity metric used ‘on top’ of the
 word embeddings. In this paper we provide comparison between two methods
 for post process improvements to the baseline DSM vectors. The counter-fitting
 method which enforces antonymy and synonymy constraints into the Paragram
 vector space representations recently showed improvement in the vectors’ capability
 for judging semantic similarity. The second method is our novel RESM
 method applied to GloVe baseline vectors. By applying the hubness reduction
 method, implementing relational knowledge into the model by retrofitting synonyms
 and providing a new ranking similarity definition RESM that gives maximum
 weight to the top vector component values we equal the results for the ESL
 and TOEFL sets in comparison with our calculations using the Paragram and Paragram
 + Counter-fitting methods. For SIMLEX-999 gold standard since we cannot
 use the RESM the results using GloVe and PPDB are significantly worse compared
 to Paragram. Apparently, counter-fitting corrects hubness. The Paragram
 or our cosine retrofitting method are state-of-the-art results for the SIMLEX-999
 gold standard. They are 0.2 better for SIMLEX-999 than word2vec with sense
 de-conflation (that was announced to be state-of the-art method for less reliable
 gold standards). Apparently relational knowledge and counter-fitting is more important
 for judging semantic similarity than sense determination for words. It is to
 be mentioned, though that Paragram hyperparameters are fitted to SIMLEX-999
 results. The lesson is that many corrections to word embeddings are necessary
 and methods with more parameters and hyperparameters perform better.

Output (English): The authors present observations on the weaknesses of the existing vector space models and list a 6-step approach for refining existing word vectors