Automatic Editing of Spoken Document forIntelligent Speech Archive
Masashi Ito1 ，Tomohiro Ohno2 ，Shigeki Matsubara3
1 Graduate School of Information Science, Nagoya University
2 Graduate School of International Development, Nagoya University
3 Information Technology Center, Nagoya University

Furo-cho, Chikusa-ku, Nagoya-shi, 464-8601, Japan
1 email: masashi@el.itc.nagoya-u.ac.jp

Abstract—As typified by World Wide Web, a lot of information
became accumulated on the Internet. However, most of thecurrently distributed information is occupied by written document. Compared with it, spoken document is hardly distributed.
Therefore, if the mechanism for distributing them can be built,
our human society will be able to share much more information.
This paper proposes a technique for editing a sentence inspoken document for the purpose of converting it into theInternet contents equipped with the accessibility and readability.
By aligning the recorded video data or speech data with theedited text on a fine level, it can be utilized as the multimedia
contents equipped with the accessibility. Our technique consistsof the following three sentence technologies: (1) paraphrase,(2) division, and (3) structuration. We implemented a spoken
document edit system based on our techniques. We conducted an
edit experiment by using lecture speech data and our techniquecould achieve high accuracy. From the results, we confirmed theavailability of our technique.

I. I NTRODUCTION
As typified by World Wide Web, a lot of information
became accumulated on the Internet and the information
produced by human beings became efficiently consumed. Oneof the key technologies of such information distribution issearch technology, and a large amount of texts have been thetargets of search. However, most of the currently distributed
information is occupied by “written documents”. Comparedwith it, “spoken documents” is hardly distributed. The routinely produced linguistic information is overwhelmingly produced by speaking rather than by writing. Therefore, if themechanism for distributing spoken documents can be built aswell as written documents, our human society will be able toshare much more information.
Although we can consider various forms to distribute spoken
documents, the form that speech data is only uploaded to theInternet is not suitable enough to reuse them. In order to understand the content of a speech, we have to listen to the speech
data and the listening is inefficient in obtaining information.
Considering the accessibility of search, it is desirable for thetranscribed text data to contain not only speech data but alsotranscribed text data. Moreover, considering the readability inbrowsing, it is necessary for the distributed data to be edited
into the text which is easy to read.

Fig. 1.

Our speech archiving system

This paper proposes a technique for editing a sentence inspoken documents for the purpose of converting it into the
Web contents equipped with the accessibility and readability. The proposal technique consists of the following threesentence technologies: (1) paraphrase, (2) division, and (3)structuration. By these processing, a spoken document canbe accumulated as a sharable document which has excellent
readability in browsing. In addition, by aligning the recorded
video data or speech data with the edited text in detail, it
can be utilized as the multimedia contents equipped with theaccessibility.
We implemented a spoken document edit system based on
our techniques. We conducted an edit experiment by using
lecture speech data and our technique could achieve highaccuracy. From the results, we confirmed the availability ofour technique.
II. C ONTENT CREATION OF SPOKEN DOCUMENTS
Figure 1 shows the composition of the content creation
system of spoken documents. Our system receives speech
data as input, and converts it into a text by ASR, and thenprovides the syntactic information to the text by morphologicalanalysis, dependency analysis, and clause boundary analysis.
By applying our sentence edit technology to the text to whichthey have generated the titles and summaries for news speechtexts by extracting the important parts from them, and haveactualized the structuration of a news database by analyzing
the relations between news. Moreover, as the similar research,
Nakagawa et al. have developed the content creation system
based on the sounds and images of the lecture in which theslides were used[1]. By recognition, summarization, divisionand indexing of lecture speech, the multimedia contents containing images, sounds and texts are created. On the other
hand, the target of our research is the general lecture speechin which news manuscripts or slide data are not used and,thus, our approach for content creation is different from thoseof the above-mentioned researches.
B. Editing of spoken texts
Fig. 2.

Outline of the speech contents on the Web

the syntactic information was provided, our system converts
it into the text which is easy to read. Furthermore, linefeed
insertion and topic segmentation are performed to the wholespoken document, and finally a synthesized voice is generated
by speech synthesis. It also becomes easy to access directly tothe partial spoken document, by aligning the edited text, thesynthesized voice, and the recorded video in a unit such as asentence.
Figure 2 shows the browsing environment of the spoken
document content. This environment has the following features.
• Providing a simulated speech and a simulated video
These prevent the outflow of the personal information bywhich a speaker can be identified.
• Showing of a set of keywords
This set of keywords enables broadly viewing the content
of a spoken document.
• Segmentation by a topic
This segmentation enables topic-by-topic browsing and issuitable for selective reuse.
• Editing of spoken document
This editing enables a spoken document to be easy toread and understand.
III. R ELATED W ORKS
In this section, we describes the related works on content
creation of speech data and editing of a spoken language
sentence.
A. Content creation of speech data
Although some trials of sharing speech data have alreadybeen performed, the following problem has been widely
recognized[4]. That is, when spoken documents are simply
accumulated without editing, its shared data is not easy tobrowse and is not convenient for a user to access. In response,
Lee et al. have actualized the speech archiving based on thesummarization and structuration for news[5]. In this research,

As a technology for transforming a text into the text whichis easy to read, some summarization techniques have beendeveloped for dialogue speech[6], [7] or news speech[8], [9].
On the other hand, our research targets the general lecture
speech.
Moreover, Yamamoto et al. have proposed summarization technique for the Diet minutes[2]. In this technique,parenthetical expressions are deleted, redundant expressions
peculiar to spoken language are deleted or paraphrased, andhonorific expressions are converted into normal expressions.
This technique is relevant to our research in the sense thatspoken language is converted into the text which is easy toread.
Furthermore, there exist some previous researches abouta technology for dividing a sentence. Kim et al. have divided a long sentence for improving the accuracy of machinetranslation[10]. Hayashi has implemented the support systemfor polishing sentences in the technical documents, and thesystem has introduced a technology for dividing a sentence[3].
The technology identifies the splittable points and semantic
relations between the sentences generated after the division,for complex sentences including “renyou-tyuushi” expressions
or conjunctions.
IV. E DITING OF S POKEN LANGUAGE SENTENCE
Figure 3 shows an example of a transcribed text of lecture
speech and its edited text. Since the transcribed text includes
the features of spoken language, if the transcribed text is
simply displayed, it is not easy to read. In contrast, the edited
text is suitable to quickly understand the whole content. By
reducing features of spoken language in the transcribed text
and specifying the structure included in the text, we cangenerate the text excellent in the readability of browsing.
Our technique realizes editing of a sentence by performing
paraphrase processing, division processing, and structuration
processing in sequence. Figure 4 shows an example of sentence edit processing. The following processing is respectively
performed.
1) Paraphrase processing
Redundant expressions in an input sentence are deleted.

でそれから今までのPKOがどちらかといえば軍隊中心のPKOであったとすればこ
れからはシビリアンですね警察官とか人権モニターとか人道援助関係者とかそういう行政の監視官とかそういう人達も加わったいわばミックス型のPKOになるであろうというふうに考えられます。西半球には米州機構というのがありま
すし欧州ヨーロッパには皆様ご承知のようなEUとか全欧州安保協力機構というのがありますしアフリカにはアフリカ統一機構というのがあるわけです。
And then, if the previous PKO is considered to have been rather the army-centered PKO, it isthought that the future PKO will become the civilian-centered PKO, as it were, the mixed typePKO in which people, such as police officers, monitors of human rights, humanitarian
supporters,
and government observers, participate.
In the Western Hemisphere, the Organization of American States exists,
and in Europe, the EU and Organization for Security and Cooperation in Europe exist,
and in Africa, the Organization of African Unity exists.

それから今までのPKOが軍隊中心のPKOでありこれからはシビリアン
 警察官
 人権モニター 人道援助関係者
 行政の監視官
そういう人達も加わった
ミックス型のPKOになるだろうと考えられます。

and then, if the previous PKO
has been the army-centered PKO
civilians such aspolice officers
monitors of human rights
parties of humanitarian supports
government observers
includes such people
it is thought that it will be mixed
type PKO
in the Western Hemisphere,
Organization of American States exists
in EuropeEU
Organization for Security andCooperation in Europe
exist

西半球には米州機構があります。
ヨーロッパには
 EU
 全欧州安保協力機構があります。
アフリカにはアフリカ統一機構があります。

in Africa, Organization of African Unity
existsFig. 3.

TABLE IR ESULT OF ANALYSIS ABOUT PARAPHRASE
paraphrased expression
Polite expressions
expressions includeing “desu” or “masu”
Spoken language
expressions peculiar to spoken language
Honorific verb
verbs which are used as honorific expressions
Collocation “toyu”
expressions including “toyu”
Completement and correction
completement of missing particles orcorrection of grammar errors
Prefixdeletion of a prefix
Conjunctiondeletion of a redundant conjunction
Demonstrative
deletion of a redundant demonstrative
renyou-tyuushi bunsetsu
paraphrase of renyou-tyuushi bunsetsu
otherstotal

number of times
112
51
32
26

27
14
14
13
13
27
329

Example of a transcribed text and its edited text

A. Paraphrase processing

Transcribed text

西半球には米州機構というのがありますし欧州ヨーロッパには皆様ご承知のようなEUとか全欧州安保協力機構というのがありますしアフリカにはアフリカ統一機構というのがあるわけです。
In the Western Hemisphere, the Organization of American States exists, and in Europe, the EU
and Organization for Security and Cooperation in Europe exist, and in Africa, the Organization of
African Unity exists.

Paraphrase

西半球には米州機構があり欧州ヨーロッパにはEUとか全欧州安保協力機構がありアフリカにはアフリカ統一機構があります。
In the Western Hemisphere, the Organization of American States exists, and in Europe, the EU
and Organization for Security and Cooperation in Europe exist, and in Africa, the Organization of
African Unity exists.

Division

西半球には米州機構があります。
欧州ヨーロッパにはEUとか全欧州安保協力機構があります。
アフリカにはアフリカ統一機構があります。
In the Western Hemisphere, the Organization of American States exists.
In Europe, the EU and Organization for Security and Cooperation in Europe exist.
In Africa, the Organization of African Unity exists.

Structuration
Edited text

西半球には米州機構があります。
ヨーロッパには・EU
・全欧州安保協力機構があります。
アフリカにはアフリカ統一機構があります。
Fig. 4.

in the Western Hemisphere,
Organization of American States exists
in Europe
Organization for Security andCooperation in Europe
exist
in Africa, Organization of African Unity
existsExample of sentence editing processing

Furthermore, spoken language expressions are converted
into written language expressions.
2) Division processing
Divisible points in an input sentence are detected, andthe sentence are divided into two or more sentences.
Moreover, the end of each sentence generated by thedivision are paraphrased into the proper expressions.
3) Structuration processing
Parallel structures in an input sentence are detected.

In order to create the paraphrase rule for automatic sentence
editing, we paraphrased the transcribed text of lecture speechby hand. We used 100 sentences extracted from seven lecturesof which speaker is different. A total of 329 paraphrases were
performed. Table I shows the breakdown of the paraphrased
expressions. The examples of the paraphrased expressions are
as follows.
• Polite expressions
しておりました (did) ⇒ していた (did)shi-te ori-mashi-ta ⇒ shi-te i-ta
• Spoken language
おっきい (big) ⇒ おおきい (big)okkii ⇒ ookii• Honorific verb
させて頂く (do) ⇒ する (do)sa-se-te itadaku ⇒ suru
• Collocation “toyu”
正 常 化 と い う こ と が 行 わ れ た (perform thenormalization) ⇒ 正 常 化 が 行 わ れ た (perform thenormalization)
seijouka-toiu-koto-ga okonawa-re-ta ⇒ seijouka-ga
okonawa-re-ta• Complement and correction
私普段から (I usually)⇒ 私は普段から (I usually)watashi fudan-kara ⇒ watashi-ha fudan-kara• Prefix
お料理を食べる (eat foods)⇒ 料理を食べる (eat foods)
o-ryouri-wo taberu ⇒ ryouri-wo taberu
Especially, “polite expressions”, “spoken language”, “honorific verb”, and “collocation ‘toyu”’ included many redundant expressions. Since these expressions appear frequently,
paraphrasing them enables the features of spoken languagein the whole sentence to be efficiently reduced. In other<Polite expressions>。 → verb(adverval form) + masu。
e.g. 今から話すところです。 → 今から話します。

verb + {n | no | mono | tokoro} + desu
ima-kara hanasu-tokoro-desu.

ima-kara hana-shi-masu.

(I will talk from now.)(I will talk from now.)<Spoken language>→ nadoe.g. 日本の本なんかを読んだ
nannkanihon-no hon-nanka-wo yon-da→ 日本の本などを読んだ(I read Japanese books, etc.)

nihon-no hon-nado-wo yon-da(I read Japanese books, etc.)<Honorific verb>→ verb(inflected form of “itadaku”)
これについて述べさせていただくし， → これについて述べるし，

verb + {se | sase} + te + itadaku
e.g.

kore-ni-tsuite nobe-sase-te-itadaku-shi

kore-ni-tsuite noberu-shi(I will describe this, and …)(I will describe this, and …)<Collocation “toyu”>
noun + toyu + noun(non-content word)e.g.

社会への復帰ということは終えて
shakai-e-no fukki-toyu-koto-wa oe-te→ noun→ 社会への復帰は終えて(The return to society was archieved, and …)

Fig. 5.

shakai-e-no fukki-toyu-koto-wa-oe-te(The return to society was archieved, and …)

Examples of the paraphrase rules and its application

expressions, the paraphrases were performed in considerationof the sense of a sentence, therefore, it is difficult to create
rules based on the surface information. From these reasons, wecreated paraphrase rules for the expressions of the four abovementioned types. Figure 5 shows examples of the created rules
and its application.
B. Division processing
For analysis about the division processing of a sentence,
we divided a sentence in the transcribed text of lecture speechby hand. Size of the created data is 4 lectures consisting of
468 sentences. In the data, the number of times by which a
sentence was divided was 299 times. About 89% of sentence
divisions are performed just after conjunctive particle “keredomo”, “ga”, “node”, “shi”, “to”, “kara”, or renyou-tyuushi
bunsetsu 1 . Here, renyou-tyuushi bunsetsu is a bunsetsu of
which the morphological information is one of the following
two cases.
• The inflected form of the rightmost morpheme of thebunsetsu is adverbial form.
e.g. “食べ” tabe, “訳で” wakede• The second rightmost morpheme and the rightmost morpheme of the bunsetsu are adverbial form and conjunctive
particle “te,” respectively.
e.g. “食べ” tabe-te，“書いて” kai-te
The above-mentioned conjunctive particles and renyou-tyuushi
bunsetsu become a predicate and, thus, they are considered to
be a strong break semantically. Table II shows the appearance
frequency of the above-mentioned conjunctive particles andrenyou-tyushi bunsetsu and the number of times by which
a sentence was divided at each point. In our research, theend boundary of the above-mentioned conjunctive particle
or “renyou-tyuushi” bunsetsu is defined as a candidate of
1 Bunsetsu is a linguistic unit in Japanese that roughly corresponds to abasic phrase in English. A bunsetsu consists of one independent word andzero or more ancillary words. A dependency is a modification relation inwhich a modifier bunsetsu depends on a modified bunsetsu. That is, themodifier bunsetsu and the modified bunsetsu work as modifier and modifyee,respectively.

TABLE IIR ESULT OF ANALYSIS ABOUT SENTENCE DIVISION

conjunctive particle “keredomo”
conjunctive particle “ga”
conjunctive particle “node”
conjunctive particle “shi”
conjunctive particle “to”
conjunctive particle “kara”
renyou-tyuushi bunsetsu
otherstotal

number of timesof division
75
68
25
12
10
4
72
33
299

number of times
of appearance
84
82
30
12
75
7
271
-

division points. However, a sentence can not necessarily be
divided at all the candidates of division points. In some cases,
the meaning of the original sentence may be lost by dividing
at the candidate. Therefore, based on dependency relations,
we established the conditions on which a sentence could be
divided.
• Bunsetsus located before the candidate of division points(except the bunsetsu located just before the point) do notdepend on a bunsetsu located after the point.
Figure 6 shows an example of a sentence which fulfills
this condition. A sequence from the start of the sentence
to the candidate of division points constitutes a independent
clause, which basically contains one verb phrase. That is, inthis example, it can be said that two simple sentences areconnected. Therefore, the sentence can be divided into two
independent simple sentences.
When a sentence does not fulfill the condition, the clausecontaining a predicate located just before the candidate ofdivision points becomes an embedded clause. Figure 7 shows
an example of a sentence which includes an embedded clause.
In case that a sentence includes an embedded clause, dividing
the sentence prevents readers from understanding the modified
bunsetsu of the bunsetsu “watashi-wa” and, thus, the readers
become unable to understand the meaning of the sentence.
However, even if a sentence contains an embedded clause,
there are some cases that the sentence can be divided. Therefore, in order to judge it, we added the following two exception
conditions. If one of the following conditions is fulfilled,
the division can be performed even if the above-mentioned
condition is not fulfilled.
1) The morpheme located just after the candidate of division points is a demonstrative, a conjunction, or a
conjunctive particle, or the clause located just after the
candidate of division point is the topicalized-element“wa” clause.
2) The dependency relation which straddles the candidateof division points has the modifier bunsetsu of which
the head morpheme is adverb or conjunction.
When the condition 1) is satisfied, the former and the latterof the sentence are considered to be connected by the coordinative relation. Figure 8 shows an example of a sentence
which satisfies the condition 1). Here, the candidate of division points is located just after bunsetsu “fukamari,” and

机の

上に 旗が
ue-ni

hata-ga

たっているので 国連に
ta-tte-iru-node

kokurenn-ni

来てしまったと 感じた。
kite-shima-tta-to

kannjita.

the
desk

on

the
flags

since theystood

to theUnitedNationshave come toI felttukue-no

欧州においては
地域協力が深まり
oushuu-ni-oite-wa
chiikikyouryoku-ga
fukamari
In Europe

regional
cooperationhas
advanced

また範囲もhani-mo

広くなっている。
hiroku-na-tte-iru.

moreoverits scope

has spreadmata(conjunction)

candidate ofdivision point

candidate ofdivision point

In Europe, regional cooperation has advanced, moreover, its scope has spread.

Since the flags stood on the desk, I felt that I have come to the United Nations.

欧州においては地域協力が深まる。また範囲も広くなっている。

机の上に旗が立っている。国連に来てしまったと感じた。

In Europe, regional cooperation has advanced.
Moreover, its scope has spread.

Since the flags stood on the desk.
I felt that I have come to the United Nations.

Fig. 8.
Fig. 6.

私はwatashi-wa

彼がkare-ga

怒ったので
oko-tta-node

すぐにsuguni

I

he

Since

immediately

got angry

帰りました。
kaeri-mashi-ta.
returned

そしてsoshite(conjunction)

thencandidate ofdivision point

Since he got angry, I immediately returned.

Fig. 7.

Example of a sentence which fulfills the exception condition 1

Example of a sentence which fulfills the condition

小笠原が

返還され

次に

沖縄が

Ogasawara

wasreturnedand
Next

Okinawa

Ogasawara-ga

henkansare

tsugini

Okinawa-ga

返還されました。
henkansare-mashi-ta.

was
returnedcandidate ofdivision point
Then Ogasawara was returned, and next, Okinawa was returned.

そして小笠原が返還されました。次に沖縄が返還されました。

Example of a sentence which includes an embedded clause

Then Ogasawara was returned.
And next, Okinawa was returned.

bunsetsu “oosyuu-ni oite-wa” is the modifier bunsetsu of thedependency relation which straddles the candidate. Therefore,
the dependency information is lost by dividing the sentence.
However, a reader can understand the meaning of a sentenceby complementing it.
When the condition 2) is satisfied, the modifier bunsetsu
of the dependency relation which straddles the candidate ofdivision points often modifies the whole sentence. Figure 9shows an example of the sentence which fulfills the condition 2). In this example, the candidate of division point islocated just after the bunsetsu “henkan-sare”, and the bunsetsu“soshite” is the modifier bunsetsu of the dependency relation
which straddles the candidate.
C. Structuration processing
In our research, the parallel structures in a sentence aredetected and specified. The detection of parallel phrases isperformed by using KNP[11]. KNP can judge whether the
dependency relation between bunsetsus is a parallel one.
Figure 10 shows an example of the detection of a parallelphrase. Based on bunsetsus of which the dependency relation
is a parallel one, the parallel phrases are detected as follows.
The final bunsetsu of the parallel phrase 1 is defined as
the modifier bunsetsu “koui-ya” of the parallel dependency
relation. The start bunsetsu of the parallel phrase 1 is definedas the leftmost bunsetsu “wa-ga kuni-ga”, which can reach thefinal bunsetsu “koui-ya” along the dependency relation paths.
If there is no bunsetsu which depends on the final bunsetsu,
the start bunsetsu is defined as the final bunsetsu itself. Parallelphrase 1 is defined as a sequence of bunsetsus “wa-ga kuniga” from the start bunsetsu “koui-ya” to the final bunsetsu.
Next, the final bunsetsu of parallel phrase 2 is defined as
the modified bunsetsu “kutsuu-wo” of the parallel dependency
relation. The start bunsetsu of parallel phrase 2 is defined as thenext bunsetsu “ta-kokumin-ni” of the final bunsetsu “koui-ya”
of the parallel phrase 1. The parallel phrase 2 is defined as theFig. 9.

Example of a sentence which fulfills the exception condition 2

sequence of bunsetsus form the start bunsetsu “ta-kokumin-ni”
to the final bunsetsu“koui-ya”.
The morphological analysis using JUMAN[12] causes manyerrors because of spoken language expressions. The errors
caused by the morphological analysis influence the detectionof parallel phrases. Therefore, among the parallel dependency
relations detected by KNP, we finally decided only the paralleldependency relation which satisfies the following condition as
the correct parallel one.
• The parallel dependency relation has the modifier bunsetsu of which the rightmost morpheme is one of thefollowing morphemes: “mata”, “to”, “soshite”, “toka”,“oyobi”, “ya”, “shi”.
V. E VALUATION
In order to evaluate the effectiveness of our proposal
method, we conducted an experiment. As the correct data,
we used 3 lectures (392 sentences) of which sentences wereedited by hand. We obtained the precision and recall in eachof paraphrase processing, division processing and structuration processing. In the following subsection, we describe theevaluation of each processing.
A. Evaluation of paraphrase processing
The precision was 76.6%, and the recall was 50.7%. In ourparaphrase processing, many redundant expressions remained,
compared with the correct data. Figure 11 shows an exampleof the redundant expressions.
These redundant expressions are accompanied by the nounsuch as “sidai” or “wake”. It is possible to handle them bycreating corresponding rules. Moreover, in the correct data,
demonstratives or conjunctions are deleted and particles areParallel

今
ima

now

我が国が
行った
行為や
他国民に
与えた
wa-ga-kuni-ga
okona-ttakoui-yatakokumin-ni
atae-tathat
ourcountrydidtheactionthat

othercountry’s
citizens

parallel phrase1

causedby ourcountry

苦痛を認識する。
kutsuu-wo
ninshikisuru.

the painrecognize

parallel phrase2

Now, I recognize the action that our country did and the pain of othercountry’s citizens caused by our county.

Fig. 10.

Example of detection of a parallel phrase

原文：それで彼は安心した次第でございます。
(Then, he felt relieved.)
正解データ： それで彼は安心しました。
提案手法： それで彼は安心した次第です。
Fig. 11.

Example of a redundant expression which was not deleted
parallel

セル画や
seruga-ya

下敷きなどを

shitajiki-nado-wo

よく
yoku

選んでいました。
eran-de-ima-shi-ta.

a celluloid
a deskoften
used to choose
picture orpad, etc
I used often to choose a celluloid picture or a desk pad, etc.
example of display

・セル画
・下敷き
をよく選んでいました。
Fig. 12.

Example of failure of the structuration

complemented. Since many of these processing are paraphrased based on contexts, it is difficult to paraphrase them
automatically by using rules. In addition, although we createdrules for the spoken language expressions which appeared inanalysis data, there is the problem in the coverage.
B. Evaluation of division processsing
The precision was 64.2%, and the recall was 74.5%. Our
division processing does not take into account the length of asentence (e.g. the number of characters). In case that a original
sentence is short or each sentence generated by dividing
the original one becomes too short, the readability is notnecessarily improved by dividing the original one. Moreover,the errors caused by our created rules were also seen. These
rules need to be scrutinized. We have to restrict the conditions
on which a sentence can be divided, by using the information
on part-of-speech and dependency relation which is obtained
from a larger size of analysis data.
C. Evaluation of structuration processing
The precision was 34.8%, and the recall was 24.6%. Boththe precision and recall were low. As the reason, there is aproblem of the accuracy of JUMAN and KNP. Although wecreated the rules for reducing the influence of errors caused
by JUMAN, the accuracy of KNP did not improve enough.
Moreover, even if the structure which was detected as a parallel
structure was semantically parallel in fact, there existed thecase that the display of the parallel structure did not correspondwith that of the correct data. Figure 12 shows an example ofthis case.

VI. C ONCLUSION
In this paper, we proposed the spoken document content which achieves the effective reuse of speech data, anda technique for editing sentences in spoken document inconsideration of the readability. Moreover, we described thefundamental technologies for actualizing the edit of a spoken
document, and implemented them. The experimental results
showed the feasibility of our technique.
Future research includes the extension of the paraphrase
rules to improve the precision and recall. Furthermore, in thedivision processing or structuration processing, it is necessary
to consider the readability. Especially, the number of characters in a sentence or a line is thought to influence the readability. We plan to design these techniques in consideration of
the factors.
ACKNOWLEDGEMENTS
This research was partially supported by the Grant-in-Aid
for Scientific Research (B) (No. 20300058) of JSPS and by
the Asahi Glass Foundation.
R EFERENCES[1] Nakagawa. S, Togashi. S，Yamaguchi. M，Fujii. Y，Kitaoka. N. UsefulContents of Classroom Lecture Speech and a Browsing System (inJapanese)，Transactions of Institute of Electronics, Information andCommmunication Engineers on Information and Systems ( Japanese
Edition) ，Vol. 91-D, No.2, pp. 238-249, 2008.
[2] Yamamoto. K, Adachi. Y. Informative Spoken Language Summarization of the Diet Minutes (in Japanese)，Journal of Natural Language
Processing, Vol．12, pp．3-30, 2005.
[3] Hayashi. Y. A Three-level Revision Model for Improving Japanese Badstyled Expressions，Proceedings of the 14th International Conference
on Computational Linguistics，Vol. 2, pp. 665-671, 1992.
[4] Lee. L, Chen. B. Spoken Document Understanding and Organization,
Signal Processing Magazine, IEEE, Vol. 22, No. 5, pp. 42-60, 2005.
[5] Lee. L, Kong. S, Pan. Y, Fu. Y, Huang. Multi-Layered Summarizationof Spoken Document Archives by Information Extraction and Semantic
Structuring, Proceedings of 9th International Conference on Spoken
Language Processing, 2006.
[6] Zhu. X, Penn. G. Summarization of Spontaneous Conversations, Proceedings of 9th International Conference on Spoken Language Processing, 2006.
[7] Murray. G, Renals. S, Carletta. J. Extractive Summarization of Meeting
Recordings, Proceedings of 9th European Conference on Speech Communication and Technology, 2005.
[8] Maskey. S, Hirschberg. J. Comparing Lexial, Acoustic/Prosodic, Discourse and Structural Features for Speech Summarization, Proceedingsof the 9th European Conference on Speech Communication and Technology, 2005.
[9] Christensen. H, Kolluru. B, Gotoh. Y, Renals. S. From Text Summarisation to Style-Specific Summarisation for Broadcast News, Proceeedingsof European Colloquium on IR Research, Vol. 2997, pp. 223-237, 2004,[10] Kim. Y.B, Ehara. T. An Automatic Sentence Breaking and Subject Supplement Method for J/E Machine Translation (in Japanese), Transactionsof Information Processing Society of Japan, Vol. 35, No. 6, pp.10181028, 1994.
[11] Kurohashi. S, Nagao. M. A syntactic Analysis Method of Long Japanese
Sentences Based on the Detection of Conjunctive Structures, Computational Linguistics, Vol. 20, No. 4, pp.507-534, 1994.
[12] Kurohashi. S, Nagao. M. Improvements of Japanese Morphological
Analyser JUMAN, Proceedings of 15th International Conference on
Computational Linguistics , Vol. 2, pp. 1123-1127, 1994.