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87135-1 N/A DOUGLAS MD-11 AND
MD11F GRAY WATER VALVE
87135-2 N/A DOUGLAS MD-11 GRAY WATER VALVE
87290-1 N/A BOEING 747-400 Valve, Shut off
87570-1 N/A BOEING 767-300 SPRING PACK EMERGENCY
9900-39 697283-1 LOCKHEED 382, 382B, 382E,
382F & 382G Flow Regulator
A3022-105 N/A LOCKHEED L-1011-385
SERIES CHECK VALVE
A5160 N/A BOEING 747 Ram Air Valve
A5160-3 N/A BOEING 747 Ram Air Valve
GA7463-3 N/A BOEING 747 (ALL); 777-200 Subber Rotary
MC14464 9914464 CESSNA PTU
MC14549-1 9912392-1 CESSNA PUMP, FIXED DISPLACEMENT
MC14549-2 9912392-2 CESSNA PUMP, HYDRAULIC
MC14549-3 9912392-1 CESSNA PUMP, FIXED DISPLACEMENT
MC14549-4 N/A CESSNA PUMP
MC14549-5 9912392-3 CESSNA PUMP, HYDRAULIC
MC15393-1 9914119-3 CESSNA PUMP, HYDRAULIC
Eaton
Aerospace Group Hydraulic Systems Division, LA
January 15, 2008 Capabilities List
FAA Repair Station FHR501L
Submittal Date:
January 15, 2009
Eaton Part Number Customer Part
Number (Manual
Entry)
Model Number Description
MC15393-2 9914119-3 CESSNA PUMP, HYDRAULIC
MC18126 N/A BOEING SWITCH, PRESSURE BASIC
MC19232-2 9912393-2 CESSNA CHECK VALVE
MC2574 1159SC-H236-1 GULFSTREAM ASSY, VALVE-RELIEF (GII)
MC6477 1159SC-H249-1 GULFSTREAM BLEED AIR VALVE
65940-2 1159SC-H-302 Gulfstream G-V Manual Operated Valve
71556-8 R9045 Lear 45 Isolation Valve
71295-2 71295-2 Water Bomber Pressure Reducer, Solenoid Operated Valve
64020-1 64020-1 GULFSTREAM II Thrust Reverse Control Valve
Eaton
Aerospace Group Hydraulic Systems Division, LA
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pages 306–314,
Singapore, 6-7 August 2009. c
2009 ACL and AFNLP
Automatically Evaluating Content Selection in Summarization without
Human Models
Annie Louis
University of Pennsylvania
lannie@seas.upenn.edu
Ani Nenkova
University of Pennsylvania
nenkova@seas.upenn.edu
Abstract
We present a fully automatic method for
content selection evaluation in summarization
that does not require the creation of
human model summaries. Our work capitalizes
on the assumption that the distribution
of words in the input and an informative
summary of that input should be similar
to each other. Results on a large scale
evaluation from the Text Analysis Conference
show that input-summary comparisons
are very effective for the evaluation
of content selection. Our automatic methods
rank participating systems similarly
to manual model-based pyramid evaluation
and to manual human judgments of
responsiveness. The best feature, Jensen-
Shannon divergence, leads to a correlation
as high as 0.88 with manual pyramid and
0.73 with responsiveness evaluations.
1 Introduction
The most commonly used evaluation method for
summarization during system development and
for reporting results in publications is the automatic
evaluation metric ROUGE (Lin, 2004; Lin
and Hovy, 2003). ROUGE compares system summaries
against one or more model summaries
by computing n-gram word overlaps between the
two. The wide adoption of such automatic measures
is understandable because they are convenient
and greatly reduce the complexity of evaluations.
ROUGE scores also correlate well with
manual evaluations of content based on comparison
with a single model summary, as used in
the early editions of the Document Understanding
Conferences (Over et al., 2007).
In our work, we take the idea of automatic
evaluation to an extreme and explore the feasibility
of developing a fully automatic evaluation
method for content selection that does not make
use of human model summaries at all. To this end,
we show that evaluating summaries by comparing
them with the input obtains good correlations with
manual evaluations for both query focused and update
summarization tasks.
Our results have important implications for future
development of summarization systems and
their evaluation.
High correlations between system ranking produced
with the fully automatic method and
manual evaluations show that the new evaluation
measures can be used during system
development when human model summaries
are not available.
Our results provide validation of several features
that can be optimized in the development of
new summarization systems when the objective
is to improve content selection on average,
over a collection of test inputs. However,
none of the features is consistently predictive
of good summary content for individual inputs.
　
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