In this paper, we formulate extractive summarization as a two step learning problem building a generative model for pattern discovery and a regression model for inference.

Then, using these scores, we train a regression model based on the lexical and structural characteristics of the sentences, and use the model to score sentences of new documents to form a summary.

Our approach differs from the early work, in that, we combine a generative hierarchical model and regression model to score sentences in new documents, eliminating the need for building a generative model for new document clusters.

Later, we build a regression model with the same features as our HybHSum to create a summary.

We keep the parameters and the features of the regression model of hierarchical HybHSum intact for consistency.

In this paper, we present a novel approach that formulates MDS as a prediction problem based on a two-step hybrid model: a generative model for hierarchical topic discovery and a regression model for inference.

We construct a hybrid learning algorithm by extracting salient features to characterize summary sentences, and implement a regression model for inference (Fig.3).

Our aim is to find features that can best represent summary sentences as described in § 5, — implementation of a feasible inference method based on a regression model to enable scoring of sentences in test document clusters without retraining, (which has not been investigated in generative summarization models) described in § 5.2.

We build a regression model using sentence scores as output and selected salient features as input variables described below:

(4), we train a regression model .

Once the SVR model is trained, we use it to predict the scores of ntest number of sentences in test (unseen) document clusters, Otest = {01, “plowstl Our HybHSum captures the sentence characteristics with a regression model using sentences in different document clusters.

The fitted logistic regression model (black line) has a statistically significant coefficient for response entropy (p < 0.001).

Figure 5 plots the relationship between the response entropy and the accuracy of our decision procedure, along with a fitted logistic regression model using response entropy to predict whether our system’s inference was correct.

A logistic regression model can capture these facts.

Therefore, we learn a linear regression model with time (an operationalization of annotation costs) as the dependent variable.

We learned a simple linear regression model with the annotation time as dependent variable and the features described above as independent variables.

This optimization may include both exploration of additional features (such as domain-specific ones) as well as experimentation with other, presumably nonlinear, regression models .