A Survey of Unstructured Text Summarization Techniques <s> d) Unsupervised Approaches <s> A novel method for simultaneous keyphrase extraction and generic text summarization is proposed by modeling text documents as weighted undirected and weighted bipartite graphs. Spectral graph clustering algorithms are useed for partitioning sentences of the documents into topical groups with sentence link priors being exploited to enhance clustering quality. Within each topical group, saliency scores for keyphrases and sentences are generated based on a mutual reinforcement principle. The keyphrases and sentences are then ranked according to their saliency scores and selected for inclusion in the top keyphrase list and summaries of the document. The idea of building a hierarchy of summaries for documents capturing different levels of granularity is also briefly discussed. Our method is illustrated using several examples from news articles, news broadcast transcripts and web documents. <s> BIB001 </s> A Survey of Unstructured Text Summarization Techniques <s> d) Unsupervised Approaches <s> We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model. <s> BIB002
These approaches determine the relevant parameters without regard to human-made summaries BIB001 . The summary is generated without any user input. Probabilistic Latent Semantic Indexing (PLSI) is an unsupervised learning method based on statistical latent class models. PLSI was applied to document clustering by Hoffman  . In contrast to standard Latent Semantic Indexing (LSI) by Singular Value Decomposition, the probabilistic variant, PLSI, has a solid statistical foundation and defines a proper generative data model. Retrieval experiments indicated substantial performance gains over LSI. PLSI was further developed into a more comprehensive Latent Dirichlet Allocation (LDA) model by Blei et al BIB002 . LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. Topic probabilities provide an explicit representation of a document. The unsupervised approaches do not require user input in deciding the important features of the document, requiring a more sophisticated algorithm to compensate for lack of human intervention. We believe unsupervised summaries provide a higher level of automation which makes them more suitable for processing Big Data.