Legal Document Summarization using
Judicial Precedent Corpus
Shinji Banno1

Shigeki Matsubara2

Masatoshi Yoshikawa1

1 Graduate School of Information and Science, Nagoya University.
2 Information Technology Center, Nagoya University.

e-mail : banno@dl.itc.nagoya-u.ac.jp

Abstract
Recently, a lot of judgments havebeen saved as electronic documentsin the field of Law. In order to utilize them effectively, the summaryof the trial needs to be given to
the original judgment data. However, the huge work for summarization is done with the help, and therefore the burden for making the summary is very large. This paper proposes a method for summarizing ajudgment automatically. The summary is created by applying SVM
to a judgment and extracting important sentences. Our method consistsof three stages: (1) acquisition ofthe features from a judicial precedent
corpus, (2) extraction of the important sentences by SVM, and (3) selection of the sentences as the summaryfrom extracted sentences. As features of SVM, we use both the morphological information and the information peculiar to a judgment. We
made an experiment using a judicial precedent corpus which we madefrom the collection of the SupremeCourt. The corpus consists of 624
documents, and about 1600 thousand
morphemes. As the result, the precision and the recall provided 52.2%
and 34.5%, respectively.

1Introduction

In the modern judiciary, the judicial precedents are very important. In a trial, the experts in Law refers to the past judicial precedents. The judge, the prosecutor, and the attorney, add their opinions or interpretations to
the judicial precedents according to each position. Especially, it is said that the judge must

respect the judicial precedents and be pursuant
to them(Nakano, 2002), so they have much effect on the judge.
The judicial precedents are described injudgments. When we understand the judicial
precedents, the user needs to select the judgment and to read them in it. Various additional information is usually provided for themin order to lighten these task of the user. For
example, the information such as a summaryof the judgment and references are provided forthe judgments in the collection of the Supreme
Court judicial precedents 1 . Such information
is used to search the proper judicial precedent.
However, the information added to thesejudgments is provided manually, and the burden of making it is very large. Above all, generating the summary of the judgment is neededto acquire the contents corresponding to the
judicial precedent of the trial, so the maker has
to understand all contents of the judgment. A
judicial precedent is a legal judgment of thejudge in the trial, and is thought as importantsentences of a judgment.
This paper proposes a method for generating the summary from the judgment. Our
method generates the summary using the important sentence extraction technique. So
far, many researchers have proposed various
methods in an important sentence extractiontask, our method uses Support Vector Machine(SVM)(Vapnik, 1995) to it. The effectivity of the SVM is shown in the domain ofnewspapers(Hirao et al., 2002), we expect theeffect in the domain of judgments.
Our method consists of three stages; acquisition of the features from the judicial precedents
corpus, extraction of the important sentencesby SVM, selection of the sentences as the summary from the result of SVM. As features, we1

http://courtdomino2.courts.go.jp/
home.nsf/ehome?OpenPage    !"()%*'+-,#$%'&% (*)#(+$,-.$
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Figure 2: An example of reasons of a judgment
use both the morphological information fromeach sentence and information peculiar to thejudgments. The generation of summaries isbased on the result of SVM. We assume thateach judgment has one summary. If the resultof SVM has no important sentence, we selectimportant sentences using the result of SVM,
and output the summarized sentences of thejudgment.
This paper is organized as follow: Section 2
describes Japanese judgment and explains thejudicial precedents corpus, Section 3 explains
the method of important sentence extraction
using the judicial precedents corpus. and Section 4 reports on the evaluation of our methodby the experiment.

2Construction of the Judicial
Precedent Corpus

2.1

Composition of a Judgment

The method for describing the judgments isdependent on the place of the court, the makerof the judgment, the number of the instance,and so on. In this paper, we accord to thedescription of judgments of the Supreme Courtof Japan.
The collection of the judgments of theSupreme Court of Japan on the Web consistsof the judgments of civil and criminal cases forthe past about 60 years. We can browse 7,708
judgments in December 2004. Figure 1 shows
an example of a judgment. The judgment ofthe collection has some additional information.
As an additional information, there are 4 elements, the basic information about the trial,
matter of the case, summary of the judgment,and references. Each element is included asbelow.
• basic information: judicial date, court
name, trial number, type, judges, etc.
• matter of the case: brief description of
the case treated in a trial• summary of the judgment: the partguessed that it’s a judicial precedent• references: decree names concerned withthe trial
In the part of contents, the judgment itself
is described. The judgment consists of 4 elements, title, court of the second instance, main
text of the judgment, and reasons. Each element is included as below.
• title: type of a trial, case name, etc.
• court of the second instance: name ofthe previous court and trial number• main text of the judgment: conclusionof the trial• reasons: process to reach a conclusion
described in main text of the judgment
Figure2 shows an example of a judgment ofreasons. As shown in Figure 2, reasons areformed the document structure according tocertain rules. Reasons can be divided intoparagraphs, and document structure symbol
expressing is provided for each paragraph. OneTable 1: Component of reasons
elementsfact relation
theme
claim ofcomplain/defendantjudgment ofcourt below
judicial precedent
concurring opinion
contra opinion

main contents
overview of the caseissue of the trial
claim of complain or
defendant in the trial
judgment and reasonsof court below
judgment of the judge
to the theme
opinion adding to
the judicial precedent
opinion object tothe judicial precedent
Figure 3: A judgment with the XML-tagsof the elements in the Table 1 is given to them.
In reasons, some parts use the html-tags orindicator in order to be distinguished from others. The summary of the judgment is generated from the part, and when we want to understand a judgment we need to understandthe part.
2.2

Specification of the Judicial
Precedent Corpus

The judgments of the collection of the Supreme
Court judicial precedents on the web are
promised to be referred using browser, and it
is difficult for the computers to understand thestructure of the judgment. If we provide tagsfor each structure and specify the border ofstructure, it is easy for the computers to process the judgment.
The abstractive information is provided forreasons of the judgments in order to constructa judicial precedents corpus. However, the display styles of the information are dependent on
the date when the judgment is made out. Someof them cannot be specified by the contents of
the judgment, so we provide some XML-tags
and specify the contents.
We show an example of a judgment with theXML-tags in Figure 3, and an example of reasons of a judgment in Figure 4.
The tags provided for our corpus are classified into one concerned with the document
structure and one concerned with the summaryof the judgment.
We provided the tags concerned with thedocument structure to the element of the judgment, such as matter of the case, summary of
the judgment, main text of the judgment, reasons, as we described in section 2.1. We prepared the XML-tags for each element, and if
the element has multiple contents, we solve itusing the attribute.
For the tags concerned with the summary,
we need to identify the position of the contents
corresponding to summary of the judgment.
Therefore, our corpus gives the attributes toboth summary of the judgment and its contents, and we identify the matching of them.
Our corpus consists of the judgments fromthe year 1989 to the year 2004. It has 624
judgments and has about 1600 thousand morphemes.

3

Summarization using the Judicial
Precedent Corpus

3.1 Outline
Our method has three stages; training SVM,
classification by SVM, selection of importantsentences. The overview of our method isshown in Fig.5.
In the training stage, we acquire the featuresfrom each sentence of the judgment in our corpus, and make a SVM model. The informationacquired from every judgment in corpus andevery sentence of the judgment is described asfeatures for each sentence. The SVM model isbased on such information.
In the classification stage, we acquire the       * $%)"   !" *   

 
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    

Figure 5: An overview of the system

Figure 4: Reasons of the judgment provided
the XML-tagsfeatures for the target judgments like the training stage. Then, We execute the SVM using
SVM model and features information, and acquire the result of classification.
In the selection of the important sentences,
the important sentences of each judgment isdecided by the result of SVM classification. If
the result of SVM in not good, the system may
output no important sentence. To avoid thesituation, we acquire the output of our systemfrom the number information getting from theresult of SVM.
3.2

Feature Selection

As features, we use both morphological information and information peculiar to the judgment.
For the morphological information, we use
all morphemes appearing in the each sentenceof the judgment. We use morphological analysis, for each morpheme, the basic form isadopted as one of the features. However, it is
not proper to treat all the morphemes as features. Among the morphemes, morphemes likepunctuation appear in almost all sentences,and morphemes like named entity, such as personal name and geographic name, appear infew sentences. A morpheme which appears
in almost all sentences has harmful effects tothe characterization of each sentence. A morpheme which appears in few sentences is efficient to the sentence. However, the probabilityof not appearing in other sentence is large, sothe effect to select as a feature is not large.
Therefore our method uses the isf (inverse sentence frequency) value, which corresponds toidf value in the domain of information retrieval, in order to delete redundant features.
For the training data, we calculate the total appearance number for each morpheme and use
the morpheme whose value is more than K asa feature. The isf value is calculated as below:
isf (t) = logN+1sf (t)N is the total number of sentences for training data, and sf (t) is the number of sentences
appeared the morpheme t. The morphemes
which appear in each sentence are sorted by
isf value, and are used if its value is from ahigh rank to the whole C as features.
Our method use both the information about
the type of the judgment and the information about the role of sentence in the judgment. In the judgment, the position of theimportant sentences is dependent on the typeof it. In our method, we investigated our corpus and turned four typical types into the features. Some judgments include the opinion ofeach judge, such as concurring opinions, additional opinions, and contra opinions, after themain subject of the judicial precedent. The
display style of these opinions is similar to themain subject of the judgment. However, thesummary of the judgment isn’t described, weprepare the features in order to distinguish themain subject from these parts.
The features adopted by our method are 0
or 1. We provide 1 if the morpheme existed,and provide 1 if each condition was fulfilled.
3.3Extraction of Important Sentences

The selection of important sentences follows
the two conditions.
1. The sentence identified as a positive example by SVM is an important sentence.
2. If the judgment has no positive example,
we decide the important sentences according to the score of SVM from a high rank
to M rank.
That is, we select the important sentencesby the result of SVM. If SVM identifies a sentence as a positive example, the sentence is an
important sentence absolutely. If SVM identifies a sentence as a negative example, the sentence is determined whether it is an importantsentence or not according to the situation of
the judgment in which it is included. In ourmethod, we assume that the judgment shouldhave important sentences. If a judgment has
no important sentence by the result of SVM,
The sentences which have the score from a highrank to M rank are identified as important sentences. Thus we acquire the output of our system.

44.1

Experiment
Outline of the Experiment

We made an experiment in order to show theeffectivity. The judicial precedents corpus isdivided into two classes, 50 documents for test
data and 574 documents for training data. According to the preliminary experiment using
features about morphological information only,
we set the each parameter respectively, K is 20,
C is 0.67. As the result, we get 3,763 features.
According to the average number of importantsentences in the corpus, the parameter M isset 2.
As an evaluation criteria, we use precision,
recall, and F-measure. The formula is described as below:
P recision =Count× 100
Sys Out

Recall =Count
× 100
I Sen

Count is the number of correct sentences,
Sys Out is the number of system outputs,
I Sen is the number of important sentences.
We use ChaSen(Matsumoto et al., 2000)
for morphological analysis and SVM Light(T.
Joachims, 1999) for SVM.
4.2

Experimental Result

The test data has 1732 sentences and 139 important sentences. The result of our method isshown in Table 2. We compared the result ofour method with the result using the morphological information only and the result adding
the judgment information to the morphological information. Both precision and recall wereimproved by appending the information peculiar to the judgment. Since the number of features concerned with the judgment is smaller
than that of morphological features, these features are effective for deciding the importantsentences. The result of our method was notgood with regard to precision, but the recall
was improved greatly, therefore we acquired agood result with regard to F-measure. However, the value is not sufficient and we need toimprove our method.

5Conclusion

This paper proposed a summarization method
for Japanese judgments using the judicial
precedents corpus. We constructed the judicialprecedents corpus from the judgments of thecollection of Supreme Court on the Web. We
got features from the corpus and generated aSVM model, and selected important sentencesfrom a judgment. For each judgment, we decided the important sentences using SVM result. We made an experiment and evaluatedour method.
In the future, we will improve our method.
We gave information on whether the sentence
belongs to the main part or not and information concerned with the result of the judgment.
From now on, we will adopt new characteristicfeatures such as information about judgment,for example, whether the trial is criminal orcivil, creation time of a judgment, and position
information of each sentence in main part. We
also think that the morphemes information isTable 2: Experimental Result
precision(%) recall(%)
morphological information
54.0
19.4
+ judgment information
58.9
23.7
our method
52.2
34.5

to be reconsidered. In our method, we selected
all morphemes appearing in each sentence ofthe judgment as a candidate feature. However, the effect for each sentence is different
from morpheme. Therefore we need to limit
the morphemes used as features, and to change
the way of giving the features according tothe kind of morphemes. Our method gave thevalue of features as a binary data. We will try
to introduce the value of a variable, for example, tf -idf , and to improve the performance.
With regard to the number of important sentences which we select from the result of SVM,
we’ll try to improve the method, for example,
changing the number according to the type ofthe judgment.

Acknowledgement
The authors would like to thank Prof. Koji
Matsuura, Prof. Tokuyasu Kakuta, Prof. Katsuhiko Toyama for their helpful comments.
This work is partially supported by the Grandin-Aid for Scientific Research of JSPS.

References
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Thorsten Joachims. 1999. Making large-Scale
SVM Learning Practical. Advances in Kernel Methods - Support Vector Learning. B.
Scholkopf and C. Burges and A. Smola (ed.)
MIT-Press.
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Tsutomu Hirao, Hideki Isozaki, Eisaku Maeda,Yuji Matsumoto. August 2002. Extracting
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Yuji Matsumoto, Akira Kitauchi, Tatsuo Yamashita, Yoshitaka Hirano, Hiroshi Matsuda, Kazuma Takaoka, Masayuki Asahara.

F-measure(%)
28.6
38.9
41.6

2000. Japanese Morphological Analysis System
ChaSen version 2.2.1.