Our results show that summaries biased by dependency pattern models lead to significantly higher ROUGE scores than both n-gram language models reported in previous work and also Wikipedia baseline summaries.

Our evaluations show that such an approach yields summaries which score more highly than an approach which uses a simpler representation of an object type model in the form of a n-gram language model.

a corpus of descriptions of churches, a corpus of bridge descriptions, and so on) and reported results showing that incorporating such n-gram language models as a feature in a feature-based extractive summarizer improves the quality of automatically generated summaries.

The main weakness of n-gram language models is that they only capture very local information aboutshofitennsequencesandcannotnnxkfllong distance dependencies between terms.

If this information is expressed as in the first line of Table l, n-gram language models are likely to

We derive n-gram language and dependency pattern models using object type corpora made available to us by Aker and Gaizauskas.

2.2 N-gram language models

they calculate the probability that a sentence is generated based on a n-gram language model.

o LMSim3 : The similarity of a sentence S to an n-gram language model LM (the probability that the sentence S is generated by LM).

The computation of t/I(e,H(v)) is based on a linear combination of a set of n-gram consensuses-based features.

For each order of n-gram, h; (e,H(v)) and h”— (e,H(v)) are defined to measure the n-gram agreement and disagreement between e and other translation candidates in H(v), respectively.

If p orders of n-gram are used in computing t//(e,H(v)) , the total number of features in the system combination will be T +2>< p (T model-score-based features defined in Equation 8 and 2x p consensus-based features defined in Equation 9).

The third line across the bottom of the figure is the number of unseen pairs using Google n-gram data as proxy argument counts.

Creating argument counts from n-gram counts is described in detail below in section 5.2.

Using the Google n-gram corpus, we recorded all verb-noun co-occurrences, defined by appearing in any order in the same n-gram , up to and including 5-grams.

For instance, the test pair (throwsubject, ball) is considered seen if there exists an n-gram such that throw and ball are both included.

C (vd, n) as the number of times 2) and n (ignoring d) appear in the same n-gram .

The Google n-gram backoff model is almost as good as backing off to the Erk smoothing model.

For each training update, we read an n-gram x = (W1, .

We also create a corrupted or noise n-gram J?

0 We corrupt the last word of each n-gram .

Aside from including dependency and n-gram relations in the scoring, we also apply and evaluate SemPOS for English.

Table 1 estimates the overall magnitude of this issue: For 1-grams to 4-grams in 1640 instances (different MT outputs and different annotators) of 200 sentences with manually flagged errors4, we count how often the n-gram is confirmed by the reference and how often it contains an error flag.

Fortunately, there are relatively few false positives in n-gram based metrics: 6.3% of unigrams and far fewer higher n-grams.

Web 1T N-gram Features

Table 3 describes the extracted n-gram features.

The influence of the Web lT n-gram features was somewhat mixed.