A well known GLM in the NLP community is logistic regression (which may alternatively be derived as a maximum entropy classifier).

In logistic regression , the response is assumed to be Binomial and the chosen link function is the logit transformation,

Our first new approach for handling differences in transcripts is an extension of the logistic regression model previously used in data fusion work, (Savoy et al., 1988).

Perhaps the most closely related proposal, using logistic regression , was made first by Savoy et al.

Logistic regression is one example from the broad class of models which GAMs encompass.

Unlike GAMs in their full generality however, logistic regression imposes a comparatively high degree of linearity in the model structure.

To model data with a binary dependent variable, a logistic regression model is an appropriate choice.

In logistic regression , we model the log odds as a linear combination of feature values 2130 .

Standard logistic regression models assume that all categorical features are fixed efi‘ects, meaning that all possible values for these features are known in advance, and each value may have an arbitrarily different effect on the outcome.

We used the 192 sets from multi-document summarization DUC evaluations in 2002 (55 generic sets), 2003 (30 generic summary sets and 7 viewpoint sets) and 2004 (50 generic and 50 biography sets) to train and test a logistic regression classifier.

Table 6: Logistic regression classification results (accuracy, precision, recall and f—measure) for balanced data of 100-Word summaries from DUC’02 through DUC’04.

Experiments with a logistic regression classifier based on the features further confirms that input cohesiveness is predictive of the difficulty it will pose to automatic summarizers.