Elimination of Multiple Modifiers in Summarization
Kiyonori Ohtake†andShigeru Masuyama

Department of Knowledge-based Information Engineering, Toyohashi University of Technology
Tenpaku-cho, Toyohashi 441-8580, JAPAN.
Email: kohtake@slt.atr.co.jp, masuyama@tutkie.tut.ac.jp
Abstract
We propose a method that summarizes a Japanese sentence. The method aims to produce a natural andreadable summary from the sentence. This method eliminates a part of multiple adnominal modifiers
including adnominal clauses by employing natural language processing tools1 : KNP (a parser), and
JUMAN (a morphological analyzer). With this proposed method, we participated in subtask A-2 (forproducing summaries to be compared with human-prepared summaries) of the TSC 2 (Text
Summarization Challenge), one of the tasks of the second NTCIR Workshop3, which aims to generatesummaries as natural as possible. The summarization system with the proposed method resulted in goodevaluations: the mean value of all evaluations held the foremost place among nine systems.
Keywords: summarization; multiple modifiers; natural summary.

1

Introduction

This study aims at generating general and natural summaryfrom a sentence. Studies to date on summarization havefocused on extracting important parts [1]. Many of them
have also employed the sentence as the unit of extraction.
However, each sentence requires a more natural andreadable summary.
Yamamoto et al. claimed, “A summarization of highquality has to consider not only a discourse analysis for one
linguistic phenomenon but also many linguistic phenomena[2].” This claim is applicable to a summarization for asentence. In other words, we have to take account ofvarious linguistic phenomena to make a natural andreadable summary of a sentence.
We propose a method that summarizes a sentence bydeleting adnominal modifiers, because such modifiers are
considered syntactically unimportant.
Eliminating
syntactical unimportant parts seems to be a practical
approach for robust and natural summarization. On theother hand, in order to detect the syntactically unimportantparts in a sentence, we have to employ a parser to analyze
the dependency structure. Unfortunately, there have been
few works on summarization methods that employ a parser.

†

The reason why is that a parser with a high accuracy andhigh speed is unavailable.
By employing a parser, we can accurately detect adnominalparts in a sentence. However, the simple elimination ofadnominal parts can cause an unnatural and unreadable
summary [3]. On the other hand, we know empirically thateliminating a part of multiple adnominal modifiers retains
the readability of a sentence. Considering this, we focus on
multiple adnominal modifiers to summarize each sentence.
We propose a summarization method, which positively
employs results of a dependency analysis by a parser, forJapanese sentences. The proposed method focuses on
multiple adnominal modifiers in a sentence and eliminatesa part of them.

2

Summarization Method Focusing on
Multiple Modifiers

In this section, we define multiple adnominal modifiers,and propose a summarization method that summarizes asentence by considering such modifiers.

2.1

Multiple Modifiers for Nouns

In Japanese, we can classify modifiers for nouns, namely
adnominal forms, into the following ten types: koso-ado-

Currently with ATR Spoken Language Translation Research Laboratories, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto
619-0288, JAPAN.
1Both JUMAN and KNP are available from http://pine.kuee.kyoto-u.ac.jp/nl-resource/index-e.html
2

http://oku-gw.pi.titech.ac.jp/tsc/index-en.html

3

http://research.nii.ac.jp/~ntcadm/workshop/work-en.html

attributes, attributes, adjectives, nominal adjectivals, noun
+ ‘no’s, noun + case marker + ‘no’s, noun + toritate(picking up)-postpositional particle + ‘no’s, adverb + ‘no’s,
relative clauses, and content clauses.

can retain the naturalness of the sentence. To eliminate apart of all multiple adnominal parts, we employ a numberof rules derived from knowledge on linguistics, and several
heuristics.

If two or more adnominal forms modify the same noun, we
recognize these forms as multiple adnominal modifiers. In
particular, when the number of adnominal forms is two, we
recognize the two modifiers as double modifiers. The
overwhelming majority are double modifiers in Japanese
texts. This is because, the forward elements of a nounphrase with three or more adnominal forms for example,“shiroi (white) nagai (long) ookina (big) te (hand),” tend
to be continuous forms, e.g. “shirokute (white) nagai [ornagakute] (long) ookina (big) te (hand).”
Therefore,
double modifiers are the overwhelming majority ofmultiple modifiers. For this reason, we address double
modifiers in this study.

We employ 36 rules for the elimination of adnominal partsof double modifiers. Each rule consists of four components,
i.e., former adnominal part, latter adnominal part, modified,and action. Table 1 shows examples of these rules.

2.2

Special Case of Double Modifiers

There is a special case of double modifiers, “adnominal
clause - noun+no - modified.” For example, “watashi-ga
kiita sakka-no hanashi (A writer's talk which I heard)” and“watasi-ga interview-shita sakka-no hanashi (A writer'stalk that I interviewed)” have the same part of speech
sequence, but their dependency structures are different asshown in Figure 1.
watashi-ga (I)kiita (heard)

watashi-ga (I)interview-shita(interviewed)
sakka-no (writer’s)sakka-no (writer’s)
hanashi (talk)hanashi (talk)Figure 1: Different dependency structures
Parsing a sentence that includes this pattern, “adnominal
clause - noun+no – modified”, tends to fail in thedependency analysis. In other words, we cannot judge
whether the sentence that includes “adnominal clause noun+no – modified” has a double modifier or not.
Accordingly, we treat this pattern as an exception of double
modifiers and if we encounter this pattern, we eliminate theadnominal clause. However, if the result obtained byeliminating the adnominal clause is obviously unreadable,
we do nothing. This is judged from the noun in “noun+no”.
If the noun is abstract, the attributive function of“noun+no” will not affect “modified,” and therefore, we donothing. Whether a noun is abstract or not is judged from athesaurus, Goi-Taikei - A Japanese Lexicon [4]. To obtainsemantic codes from Goi-Taikei, we employ ALTJAWS
Ver.2.0: a morphological analyzing library for Japanese.

2.3 Summarization Method
When we eliminate such multiple adnominal parts, thequestion is obviously what portions should we eliminate. If
we eliminate all of the multiple adnominal parts, thesentencewouldbeunnatural,
unreadable,orincomprehensible. However, eliminating only some parts

The basic rule of elimination is that longer or more
complex parts should be unremoved, because they work
more attributively than others do. That is, the deletion ofsuch longer or more complex parts will lead to unnaturaland unreadable summaries.
The heuristics are employed to avoid influences caused byparsing errors, and provide a robust summarization method.

3

Summarization System

We implemented a summarization system consisting of thefollowing two components: a module that selects importantsentences, and a module that summarizes each sentence.
The latter uses the above elimination method of double
modifiers.
We employed a morphological analyzer,
JUMAN, and a parser, KNP.
Through a formal run of subtask A-2 of the TSC (Text
Summarization Challenge) in the second NTCIR (NIINACSIS Test Collection for IR Systems) Workshop, 20%
and 40% of the summaries of 30 newspaper articles were
evaluated.
When participating in subtask A-2 of the TSC, it is not
sufficient to only eliminate double modifiers. Considering
this, we implemented five summarization methods for eachsentence besides the proposed method as follows:
Elimination of supplementary explanations: Eliminate
additional information expressed in parentheses, etc. For
example, 公職選挙法(一九九条)違反 → 公職選挙法違反 (a violation of the election law (the 199th article) → a
violation of the election law )Elimination of the first sentence in direct quotations:Eliminate the first sentence of a direct quotation if two or
more sentences compose a direct quotation. For example,
そのうえ「明日の公式試合には出なくてええ．背番号も返せ」と言われたという。 → そのうえ「背番号も返せ」と言われたという。(In addition, it is said
that he said, “You don’t need to participate in tomorrow’s
regular game. Return the uniform number.” → In addition,
it is said that he said, “Return the uniform number.”)
However, this elimination is canceled if a demonstrative is
included on and after the second sentence, because thedemonstrative may refer to a part of the first sentence.
Elimination of direct quotations: There are somesituations in which a summary of a direct quotation follows
the direct quotation. These patterns are indicated byTable 1: Examples of elimination rules for double modifiers
former…no--…toiu
adnominal clause---latter…no--…no
adjective…na

Modified

Action--…tono…no--koto…

Do nothing
Do nothing
Eliminate the latter part
Eliminate the latter part
Do nothing

Here, ‘---’ means any pattern.

Kodama et al. [5], and we employ these patterns to detect
direct quotations able to be eliminated. For example, 検察
側は「捜査段階で事実を認めていた」と主張して，タイミングを計って証拠申請する構え．→ 検察側はタ
イ ミ ン グ を 計 っ て 証 拠 申 請 す る 構 え ． (Prosecutors
claimed, “the fact was accepted in the criminalinvestigation stage”, and they are planning to make theevidence application at the precise time. → Prosecutors areplanning to make the evidence application at the precise
time.)
Elimination of illustrations: Illustrations are considered
to be modifiers in a broad sense, and it is assumed thatmeanings are not changed even if they are eliminated.
Therefore, for example, we eliminate the “...nado no (suchas)” in the pattern “...nado no + noun.” We also eliminate“...nado de”, which depends solely on a declinable word.
For example, 経済や外交戦略などの専門知識はもとよ
り ， → 専 門 知 識 は も と よ り ， (... as well as special
knowledge, such as economics and diplomatic strategies→ ... as well as special knowledge), 既に蔵相・外相会合などで取り上げられている→既に取り上げられている(It has already been taken up by the finance minister, at theforeign minister meeting, etc. → It has already been takenup.)
Paraphrasing by table: We paraphrase conjunctions at
the beginning of each sentence or expressions at the end ofeach sentence into shorter expressions or eliminate them.
For example, …決まらないようだ．→ …決まらない．
(It seems that ... is not decided. → ... is not decided.), そん
な中，…． → …. (Under the circumstances, …. →….)We have proposed some summarization methods for thesentences above. However, these summarization methods
alone cannot create the short summaries required bysubtask A-2 (20% and 40% summaries). We therefore
implemented another summarization method by sentence
selection [5]. This method utilizes surface information of
texts and some heuristics. The sentences of each text areplaced in order by a sentence selection based summarizer,
and they are then summarized by the summarization
methods mentioned above. The overview of the processingof the summarization system (sentence selection basedsummarizer + sentence summarizer) is shown as follows:

1. Place sentences in the order of importance by thesentence selection based method.
2. Adopt the summarization methods for each sentence.
3. Select sentences in the order of importance to make asummary.
4. Output the summary and terminate the processing.

4

Evaluation

For subtask A-2 of the TSC, two evaluations were carriedout. One was a subjective evaluation and the other was acontent-based evaluation. Both evaluations were done on
two different summary lengths, i.e., 20% and 40%. The
targets of the summarization were 30 newspaper articles intotal from Mainichi Shimbun, one of the major newspapers
in Japan.
In the subjective evaluation, an evaluator read thesummaries produced by each summarization system. Then,the evaluator evaluated and scored them in terms of how
readable they were, and how much the system covered theimportant contents of the original articles. The subjective
evaluation was done by rating each summary on a scale of
one to four (1, 2, 3, or 4, where 1 is the best).
In the content-based evaluation, morphological analysis
was done on the system results and human summaries, andonly content words (morphemes), i.e., nouns, verbs,
adjectives, and undefined words, were selected. Moreover,
the term weights in each summary were calculated by thetf ⋅ idf measure. Then, the similarities between the
document vectors of the human summaries and the system
results were each computed by taking the cosine of theangle between the vectors.
Table 2: Results of subjective evaluation
length
20%
20%
40%
40%

type
Readability
Content
Readability
Content

value(average)
2.53(3.16)
2.93(3.24)
2.73(3.05)
2.77(3.12)

ranking1
1
3
1

In addition, two kinds of human summarization wereadopted. One was freely created (FREE), and the other was
created by the extraction of important parts (PART). In thecontent-based evaluation, therefore, the two summaries,
20% and 40%, were compared with the results of the twokinds of human summarization, FREE and PART.

First, we show the results of the subjective evaluation in
Table 2. The results show the average values of the 30
summaries by the system. Ten systems, including a baseline system, participated in this evaluation.

results show that the elimination of double modifiers
contributes to the level of compression. Meanwhile, we
found an unnatural elimination of double modifiers with thepattern: “adnominal clause - noun+no – modified”.

Second, we show the results of the content-based
evaluation in Table 3. The results also show the averagevalues of the 30 summaries by the system. A total of 11
systems, including two base-line (reference) systems,
participated in this evaluation.

Rigid evaluations of each method are left for future work.

Table 3: Results of content-based evaluation
length
20%
40%
20%
40%

type
FREE
FREE
PART
PART

value(average)
0.4727(0.4418)
0.6483(0.6065)
0.5137(0.4740)
0.6608(0.6342)

ranking1
1
1
1

Table 4: Comparison of six methods
method
(1)
(2)
(3)
(4)
(5)
(6)

5

number oftimes to work
61
335
7
1
6
89

number ofcharacters deleted
729
662
277
21
126
314

Discussion

In the implemented system, in order to prevent theelimination of important information, and to retain thereadability of summaries, prudent eliminations as opposed
to bold eliminations are performed. This strategy is
successful and results in good evaluations.

6

We proposed a method that naturally summarizes aJapanese sentence. We implemented a summarization
system with this proposed method. In addition, we
participated in subtask A-2 of TSC Task to evaluate theproposed method. The strategy we took, i.e., preventing
the elimination of important information and retaining thereadability of a summary, was found to be successful andresulted in good evaluations.

Acknowledgements
The authors are grateful to Nippon Telegraph andTelephone Corporation for permitting us the use of
ALTJAWS Ver.2.0, a morphological analyzing library forJapanese, to obtain semantic codes from Goi-Taikei.

References
1Mani, I. and Maybury, M. T. (eds.): Advances inAutomatic Text Summarization, MIT press, 1999.

2

Yamamoto, K., Masuyama, S. and Naito, S. GREEN:
An experimental system generating summary ofJapanese editorials by combining multiple discourse
characteristics. Journal of Natural Language
Processing, 1995, 2(1), pp. 39 – 55, (in Japanese).

3

Mikami, M., Masuyama, S. and Nakagawa, S. A
summarization method by reducing the redundancy ofeach sentence for making captions of newscasting.
Journal of Natural Language Processing, 1999, 6(6),pp. 65 – 81, (in Japanese).

4

Ikehara, S., Miyazaki, M., Shirai, S., Yokoo, A.,
Nakaiwa, H., Ogura, K., Oyama, Y. and Hayashi, Y.:
Goi-Taikei – A Japanese Lexicon, Iwanami Publishing,
CD-ROM edition, 1999, (in Japanese).

5

Kodama, M., Kataoka, A., Masuyama, S. andYamamoto, K. Automatic extraction of summary
knowledge using direct quotation expression. In
Proceedings of the 4th Annual Meeting of The
Association for Natural Language Processing. 2000,
pp. 241 – 244, (in Japanese).

6

Ohtake, K., Okamoto, D., Kodama, M. and Masuyama,
S. Yet another summarization system with two
modules using empirical knowledge. In Proceedings
of
NTCIR
Workshop2
Meeting.
2001,
http://research.nii.ac.jp/~ntcadm/publication1-en.html.

However, the compression ratio (ratio of summary length
to source length) without the sentence selection based
method for the 30 articles is about 91%.
Accordingly, the quality of summaries produced by the
implemented system depends on the sentence selection
based method. Despite this dependence, the subjective
evaluation results show that our proposed methods work
sufficiently.
We introduced six methods to summarize a sentence, (1)Elimination of double modifiers, (2) Elimination of
supplementary explanations, (3) Elimination of the first
sentence in direct quotations, (4) Elimination of direct
quotations, (5) Elimination of illustrations, and (6)Paraphrasing by table. What portions of these methods are
the most effective? To answer this question, we made acomparison among these six methods to the 30 articles insubtask A-2, and the results are shown in Table 4. The

Conclusion