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and background inputs do not lead to better
correlations than those obtained using the update
input only. The best performance from combined
features is given by the linear regression metric.
Although the correlation of this regression feature
with pyramid scores (0.80) is comparable to JS divergence
with update inputs, its correlation with
responsiveness (0.67) is clearly lower. These results
show that the term distributions in the update
input are sufficiently good predictors of content
for update summaries. The role of the background
input appears to be negligable.
8 Discussion
We have presented a successful framework for
model-free evaluations of content which uses the
input as reference. The power of model-free evaluations
generalizes across at least two summarization
tasks: query focused and update summarization.
We have analyzed a variety of features for inputsummary
comparison and demonstrated that the
strength of different features varies considerably.
Similar term distributions in the input and the summary
and diverse use of topic signatures in the
summary are highly indicative of good content.
We also find that preprocessing like stemming improves
the performance of KL and JS divergence
features.
Very good results were obtained from a correlation
analysis with human judgements, showing
that input can indeed substitute for model summaries
and manual efforts in summary evaluation.
The best correlations were obtained by a single
feature, JS divergence (0.88 with pyramid scores
and 0.73 with responsiveness at system level).
Our best features can therefore be used to evaluate
the content selection performance of systems
in a new domain where model summaries are unavailable.
However, like all other content evaluation
metrics, our features must be accompanied by
judgements of linguistic quality to obtain wholesome
indicators of summary quality and system
performance. Evidence for this need is provided
by the lower correlations with responsiveness than
the content-only pyramid evaluations.
The results of our analysis zero in on JS divergence
and topic signature as desirable objectives to
optimize during content selection. On the macro
level, they are powerful predictors of content quality.
These findings again emphasize the need for
always including linguistic quality as a component
of evaluation.
Observations from our input-based evaluation
also have important implications for the design of
novel summarization tasks. We find that high correlations
with manual evaluations are obtained by
comparing query-focused summaries with the entire
input and making no use of the query at all.
Similarly in the update summarization task, the
best predictions of content for update summaries
were obtained using only the update input. The
uncertain role of background inputs and queries
expose possible problems with the task designs.
Under such conditions, it is not clear if queryfocused
content selection or ability to compile updates
are appropriately captured by any evaluation.
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