5. The system of claim 4 , wherein selecting the representative sentence for the relevant topic further comprises utilizing cosine similarity with term frequency-inverse document frequency weighting to determine the relevance to the relevant topic of individual sentences from the plurality of sentences assigned to the relevant topic and parsed from customer reviews regarding the particular item.
4. A system for summarizing customer reviews regarding a particular item, the system comprising: one or more application servers configured to receive, from a plurality of customers, customer reviews for a category of items comprising the particular item and items of a similar type; and a quote extraction module executing on the one or more application servers and configured to aggregate the customer reviews for the category of items, parse a plurality of sentences from the aggregated customer reviews, wherein the parsing comprises breaking compound sentences in the customer reviews based on one or more of commas, coordinating conjunctions, or transition terms, classify the plurality of sentences by sentiment by utilizing a logistic regression classifier trained using sentiment scores of terms generated based on sentences contained in training data and manually labeled as to sentiment, wherein positive, negative and mixed sentiment scores of the terms are used as positive coefficients in the logistic regression classifier and neutral sentiment scores of the terms are used as negative coefficients in the logistic regression classifier, remove those sentences having a neutral sentiment from the plurality of sentences, generate a list of topics from the plurality of sentences utilizing latent Dirichlet allocation, assign each sentence in the plurality of sentences to a topic in the list of topics, select a relevant topic from the list of topics for a particular item, wherein the relevant topic comprises the topic from the list of topics assigned to the most sentences parsed from the customer reviews regarding the particular item, select a representative sentence from the plurality of sentences assigned to the relevant topic and parsed from customer reviews regarding the particular item, the representative sentence having a highest relevance to the relevant topic and expressing a majority sentiment from among the plurality of sentences assigned to the relevant topic and parsed from customer reviews regarding the particular item, and cause the selected representative sentence to be sent to a customer computing device, wherein the selected representative sentence is presented to another customer on the customer computing device.