IMPROVING HMM-BASED EXTRACTIVE SUMMARIZATION FOR MULTI-DOMAINCONTACT CENTER DIALOGUES
Ryuichiro Higashinaka†, Yasuhiro Minami‡ , Hitoshi Nishikawa† , Kohji Dohsaka‡ ,Toyomi Meguro‡, Satoshi Kobashikawa† , Hirokazu Masataki† ,Osamu Yoshioka†, Satoshi Takahashi† , and Genichiro Kikui†† NTT Cyber Space Laboratories, NTT Corporation‡NTT Communication Science Laboratories, NTT Corporation
ABSTRACT

tact center dialogues. With this approach, domain-speciﬁc
operator-caller exchanges are automatically modeled frommulti-domain dialogue data in order to perform extractive
summarization of calls [4]. In our method, to summarize
a dialogue of a given domain, we use Viterbi decoding [5]to select utterances that are most likely to be related to thatdomain in order to make a summary.
Since we used Viterbi decoding to extract all domainrelated utterances, a shortcoming of our method was its inability to control compression rates, which has greatly limited
the method’s applicability. This paper tackles this problem byutilizing, instead of Viterbi decoding, the forward-backward
algorithm [5] to assign utterances with posterior probabilities
that indicate how likely they are to be related to a certain domain. Then, we regard such probabilities as the real-valued
importance of utterances and formulate the summarization
problem as the maximum coverage of important utterances
and important words within a predeﬁned length. Then, we
solve the problem by using integer linear programming (ILP),
which can ﬁnd the optimal extractive summarization results
under constraints [6].
In Section 2, we brieﬂy describe our previously proposed
HMM-based summarization method and explain the improvements we made to enable the control of compression rates indetail. In Section 3, we describe the dialogue data for training our HMMs as well as the reference data we created fortesting. In Section 4, we describe the summarization experiment we performed and present the results. In Section 5, weconclude the paper.

This paper reports the improvements we made to our previously proposed hidden Markov model (HMM) based summarization method for multi-domain contact center dialogues.
Since the method relied on Viterbi decoding for selecting utterances to include in a summary, it had the inability to control compression rates. We enhance our method by using theforward-backward algorithm together with integer linear programming (ILP) to enable the control of compression rates,
realizing summaries that contain as many domain-related utterances and as many important words as possible within apredeﬁned character length. Using call transcripts as input,
we verify the effectiveness of our enhancement.
Index Terms— Natural languages, Natural language interfaces, Hidden Markov models
1. INTRODUCTION
Automatically summarizing calls is one of the important goalsof contact centers because it allows human operators to lookback on their exchanges with customers to improve service
skills. It also allows supervisors to check the activity of individual operators for ﬁnding ways to improve customer satisfaction. Figure 1 shows a sample call (transcript) in theﬁnancial domain. Since such a dialogue can be quite long,
our aim is to create a short extractive summary, such as theone shown in Fig. 2. Although there has been an emergenceof work that uses natural language processing techniques toimprove the productivity of contact centers [1, 2], little work
has focused on the summarization of calls. One exception
is [3], which uses manually created heuristic rules to extract
key utterances, such as those related to caller intentions or theacceptance/loss of orders, and then uses those utterances tocreate call logs.
In that approach, however, experts must create rules andmust do so for each business domain, which is costly andproblematic in scalability. As an alternative, we have beenpromoting an approach based on hidden Markov models(HMMs) for extractive summarization of multi-domain con-

978-1-4244-7903-0/10/$26.00 ©2010 IEEE

2. HMM-BASED EXTRACTIVE SUMMARIZATION
2.1. Previous Method
Our previously proposed HMM-based extractive summarization method [4] used HMMs to model domain-speciﬁcsequences from multi-domain dialogue data. Although amethod for HMM-based extractive summarization [7] had
been reported before ours, it can only be applied to a single
domain and training data have to be created for each domain.

61

SLT 2010

OPE:
CAL:
CAL:
CAL:
OPE:
OPE:
CAL:
OPE:
OPE:
CAL:
OPE:
CAL:
OPE:
OPE:
CAL:

Thank you for waiting. This is Sakura at Wakaba Life
Tokyo contact center.
Excuse me.
It’s about my life insurance contract.
I’d like to change my plan.
Certainly.
..
.
From next month, the payment will be 25000 yen permonth. Is that okay with you?
Yes.
Right.
The new contract will come into effect from next month.
From next month?
Is that all right?
Yes, that’s ﬁne.
Okay.
Okay then. Thank you for calling.
Thank you.

HMM for
Domain 1

OPE:
CAL:
OPE:
OPE:
CAL:
OPE:

HMM for
Domain 1

HMM forall domains

HMM for
Domain 2(Domain 0)

Fig. 3. Topologies of ergodic (left) and ergodic-cs (right).
Make pairs again
and retrain
HMM for
Domain 1

HMM for
Domain 2

HMM forall domains

HMM forall domains

Retrain withthe data of
Domain 1

Concatenate
all pairs
Retrain withFor HMM for all domains,the data ofprobabilities are
Domain 2
averaged in concatenation(Domain 0)(Domain 0)

HMM for
Domain 1

HMM for
Domain 2

HMM forall domains(Domain 0)

Fig. 4. Process of concatenated training. For simplicity, thenumber of domains is depicted as two.

Fig. 1. Sample dialogue in the ﬁnancial domain. OPE andCAL denote operator and caller, respectively. There are 70 utterances (880 characters) in this transcript when unabridged.
The dialogue was originally in Japanese and was translatedby the authors.
CAL:
CAL:
OPE:

HMM for
Domain 2

ergodic with common states (ergodic-cs): This variation
bears similarity to the background model [9]. Here,in addition to ‘ergodic’, it additionally has an HMM
trained from the utterances of all domains (see Fig. 3
for the topology). Hence, this additional HMM canrepresent common utterance sequences across all domains and makes it possible for the HMM to distinguish domain-speciﬁc sequences from common onesin the case of decoding. Since this additional HMM
represents no speciﬁc domain, we call it ‘an HMM fordomain 0’ and call its states ‘common states’. Similarly to ‘ergodic’, all states in this HMM are connectedto each other with equal initial/transition probabilities.

It’s about my life insurance contract.
I’d like to change my plan.
Your current plan is the “Iki-Iki (lively) Life EX standard plan”, is this right?
Sure. Then, could you tell me the plan you would liketo change to?
I’d like to change to the “Iki-Iki (lively) Life EX premium plan”.
Certainly.
The new contract will come into effect from next month.
Okay.
Okay then. Thank you for calling.

concat: This variation uses concatenated training [10] to optimize the transition probabilities between the states ofdifferent HMMs. Figure 4 illustrates its process. For
K domains, an HMM for domain k (1 ≤ k ≤ K)
is paired ergodically with a copy of an HMM for domain 0 and retrained with the data of domain k. This
retraining reassigns the data, and, as a result, common
utterance sequences become more likely to be generated from the HMM for domain 0 and domain-speciﬁcsequences in turn become more likely to be generated
from the HMM for domain k. Then, the K paired
HMMs are merged using the HMM for domain 0 as
a pivot with the probabilities of the HMM for domain
0 averaged over K. If the ﬁtting of all HMM pairs has
not covered against training data, the HMMs are separated into pairs again and retrained. Here, the transition
probabilities of the HMM for domain 0 is summed andredistributed between the pairs.

Fig. 2. Reference summary for the dialogue in Fig. 1.
In contrast, our method only requires dialogue data with domain labels and the training is done simultaneously for all
domains.
Given the dialogue data of multiple domains, we ﬁrst
model utterance sequences of each domain using an HMM
and combine such HMMs into a single HMM. Here, we assume that each HMM has a certain number of states that emit
utterances of an operator and the same number of states for
those of a caller. These states are connected ergodically andthe training is done using the EM-algorithm. We use this typeof HMM because it has been shown to be effective for modeling two-party conversations [8]. In combining the HMMs
into a single HMM, we had three topologic variations:ergodic: This variation connects all states in the HMMs
ergodically with equal initial/transition probabilities(see Fig. 3 for the topology). In this paper, connecting
HMMs means connecting all states in the HMMs.

In our previous work [4], we trained these three types of
HMMs using multi-domain contact center dialogues. Then,to summarize a dialogue known to belong to domain k, we62

word j in the i-th utterance is included in the summary. Here,wij is derived byused Viterbi decoding to decode an input utterance sequence
into domain labels that indicate to which domain each utterance is most likely to be related. Finally, we selected allutterances in the dialogue that were given domain k labels.
Since we selected all utterances having a certain domain label, a shortcoming of our previous method was its inability
to control the compression rates. Note that, although concat achieved the best results in [4], we reconsider all threevariations in this paper because we use a different decoding
algorithm and different references (see Section 3).

wij = weight(Ui ) · weight(wj )where ‘weight’ is a function that returns the importance of
its argument, Ui is the i-th utterance, and wj is the j-th wordin the entire vocabulary within the dialogue. Here, for theweight of Ui , we use the posterior probabilities. For theweight of wj , we use its term frequency within the dialogue,
or as we show later, we can also employ an external dictionary to create a custom weight function. In maximizing theobjective function, we have four constraints. First, we have2.2. Enabling to Control Compression Rates
One simple solution to enabling compression-rate control is
to apply standard extractive summarization methods to theextracted utterances by Viterbi decoding. For example, using ILP [6], we can optimally select utterances that cover as
many important words as possible. However, this may not beappropriate because the utterances not selected by the Viterbi
decoding but that still contain important words would not be
included in the summary. In addition, selecting utterances
based only on its important words means that the importanceof an utterance is determined solely by its words, which may
be too simplistic because the importance of words is likely todepend on the context of the dialogue.
We argue that an utterance has certain real-valued importance (not a binary value as in Viterbi decoding) dependingon the context and that the real-valued importance affects theimportance of words in the utterance. For example, utterances concerning callers’ requests and decisions should bevalued more highly than others; therefore, the words in suchutterances should be more important. For such importanceof utterances, we propose to use the posterior probabilitiesof the states of the HMM for the target domain. The posterior probabilities can be calculated by using the forwardbackward algorithm [5], and they represent how likely theutterances may have been generated from the HMM of thedomain in question. We can regard the posterior probabilities as the importance of utterances because high-probability
utterances should represent key utterances characterizing thedomain.
Following this idea, we formulate the summarization
problem as the maximum coverage of important utterances
and important words. Although our formulation is similar tothat in [11], which uses position information for weighting
the words, our formulation is different in that we take into
account the ﬂow of dialogue that can be learned from data.
In our formulation, we perform the maximization using
the following objective function:argmax
mij wij zij
(1)
i

(2)

xi , zij ∈ {0, 1} (∀i, j)(3)which constrains the range of decision variables. Here, xi
is a binary value representing whether the i-th utterance isincluded in the summary. It is used in the second constraint
li xi ≤ L
(4)iwhich limits the character length of the summary. Here, li isthe character length of the i-th utterance and L is the length
limitation. Our third constraint is
xi ≥ zij (∀i, j)(5)which represents the inclusive relationship between utterances and the words in them; that is, if the i-th utterance
is not included in the summary, words in the i-th utterance
must not be included in the summary. Finally, to reduce theredundancy of the output summary, we have
mij zij ≤ 1 (∀j)(6)
iwhich means that if the same words are included in the summary more than once, only one of their weights (i.e., the highest one for maximization) is used in the objective function.
2.3. Summarization Procedure
Using our three types of HMMs and our ILP formulation, ourHMM-based extractive summarization method is performed
as follows. See Fig. 5 for an illustration of the summarization
ﬂow.
Let D (d1 . . . dN ) be the entire set of contact center dialogues, DM k (DM k ∈ DM, 1 ≤ k ≤ K) be the domain
assigned to domain k, and Udi ,1 . . . Udi ,H be the utterances in
di . Here, H is the number of utterances in di . To facilitate
the treatment of utterances by HMMs, we assume that the utterances have been converted into topic labels Tdi ,1 . . . Tdi ,Hin advance. See [4] for how we do this by means of latent
Dirichlet allocation (LDA). Basically, the conversion is thesame as that used in [7], in which ‘content topics’ are assigned

jwhere mij is a binary value representing whether the i-th utterance contains word j, wij is the weight of word j in thei-th utterance, and zij is a binary value representing whether

63

Table 1. Training data statistics.

INPUT: A dialogue in Domain k with length limitation L
Domain 1
Utterance sequence
Assign topic labels
using LDATopic label sequence
Forward-backward
algorithm…

Contact
Center
Dialogues

FIN
ISP
LGU
MO
PC
TEL
ALL

Domain K
Model topic label
sequences

HMM for Domain 1 HMM for Domain K…..

HMM for all domains (Domain 0)

OUTPUT: summary

Weight function for words

Fig. 5. Summarization ﬂow.
to newswire sentences. From D, we train the HMMs to model
the sequences of topic labels.
/ D) be the input dialogue, DM (dj ) (∈ DM ) be
Let dj (∈the table for obtaining the domain label of dj , L the length
limitation, and Udj ,1 . . . Udj ,Hdj be the utterances in dj ,where Hdj is the number of the utterances. After converting
the utterances into topic labels Tdj ,1 . . . Tdj ,Hdj , we performthe forward-backward algorithm to derive the posterior probabilities Pdj ,1 . . . Pdj ,Hdj for DM (dj ). At the same time, we
instantiate the weight function for words, which returns theterm frequency (TF) for a content word in dj . Alternatively,
we can use the following weight function for words:
weight(wi,DM k ) =log(P(wi |DM k ))log(P(wi |DM \DM k ))utts/dialogue
148.42108.27108.83125.13167.14139.55131.17chars/dialogue
1868.371535.141179.75
1438.33
1891.201489.58
1542.32

ernment Unit (LGU), Mail Order (MO), PC support (PC),and Telecommunication (TEL).
We collected 391 and 307 dialogues in two separate occasions. All dialogues have been manually transcribed. Table 1
shows the statistics for the initial 391 dialogues. It can be seenthat dialogues can be quite long with over 100 utterances and1000-2000 characters per dialogue; hence the need for summarization. Reserving this initial set for training our HMMs,
we created reference data using the remaining 307 dialogues,
which had statistics similar to the training data. We randomly
sampled 40 dialogues for each domain (240 dialogues in total) and had a text analyst make 250- and 500-character summaries by selecting utterances from the transcripts. See Fig. 2
for a sample reference.
The analyst was given the instruction: “Assume you are
the operator and make a summary by selecting utterances sothat both you and your supervisor would be able to easily
grasp what took place in a dialogue”. She was also instructedto choose as many utterances as necessary within a given
length, although she was also allowed not to select utterances
forcefully as long as the total summary length exceeded 80%of the target length. It took appropriately two weeks to create
the reference summaries.
To investigate the inter-annotator agreement, we also had
a second analyst create summaries for half the test dialogues(20 dialogues for each domain; 120 dialogues in total). Theinter-annotator agreement that the two analysts chose thesame utterance in Cohen’s κ was 0.43 and 0.53 for 250and 500-character summaries, respectively. Although we acknowledge that the agreement is not very high and we needmore concrete instructions for making summaries, the kappavalue of over 0.4 indicates that we have moderate agreement.
We use the 240 summaries created by the ﬁrst analyst as ourreference data in the following experiment.
Note that in [4], we used the scenarios as references.
Using the F-measure (the harmonic mean of the precisionand recall), we performed the evaluation by assessing how
accurately the output summaries contained the content words
in the scenarios. We used scenarios as references under theassumption that they should contain the basic content exchanged between an operator and a caller. However, strictly
speaking, this assumption may not hold. Therefore, in thiswork, we decided to prepare references manually and performthe most straightforward evaluation; that is, we calculate how

Select utterances using ILP
Posterior probabilities for Domain k# dialogues
59
64
76
70
56
66
391

(7)

where P(wi |DM k ) denotes the occurrence probability of wi
in the dialogues of DM k , and P(wi |DM \DM k ) denotes
the occurrence probability of wi in all domains except DM k .
This log likelihood ratio estimates how much a word is characteristic of a given domain and therefore makes it possible
to more accurately estimate the importance of words than the
TF. Finally, using the posterior probabilities and the weightfunction, we select the utterances in dj within L charactersthat maximize our objective function [cf. eq. (1)].
3. DIALOGUE DATA AND REFERENCES
We collected simulated contact center dialogues conducted
between novice users (as callers) and expert operators who
had experience of working at contact centers [4]. They
talked over telephones in separate rooms. The operators were
given manuals and the users were given realistic scenariosthat showed why they were calling and what they needed.
Each scenario had a description of approximately 300 characters. The dialogues ranged over six business domains:
Finance (FIN), Internet Service Provider (ISP), Local Gov-

64

Table 2. The results in F-measure for 250-character summaries averaged over the 240 test dialogues. The letters next to Fmeasure values indicate statistical signiﬁcance over others by a parametric paired t-test; ‘aa’ denotes statistical signiﬁcance over(a) with p< 0.01 and ‘a’ with p< 0.05, and likewise for ‘b’ through ‘f’. The best value is indicated by bold font.
ergodic
ergodic-cs
concat(a) even-TF
0.190
0.190
0.190

(b) viterbi-TF
0.236aa
0.241aa
0.237aa(c) prob-TF
0.232aa
0.236aa
0.258aabb(d) even-DD
0.249aac
0.249aa
0.249aa(e) viterbi-DD
0.259aabbcc
0.268aabbccdd
0.262aabbd(f) prob-DD
0.260aabbccd
0.265aabbccdd
0.271aabbcdd

Table 3. The results in F-measure for 500-character summaries. See the caption of Table 2 for the notations.
ergodic
ergodic-cs
concat(a) even-TF
0.379
0.379
0.379

(b) viterbi-TF
0.405aa
0.411aa
0.400aa(c) prob-TF
0.406aa
0.412aa
0.437aabbdee(d) even-DD
0.423aabbcce
0.423aabce
0.423aabbee(e) viterbi-DD
0.414aab
0.414aa
0.409aa(f) prob-DD
0.430aabbccee
0.435aabbccddee
0.433aabbdee

be seen that the variations that use the posterior probabilities(i.e., prob and prob-DD) outperform others in most cases,
especially for 500-character summaries, although there is
no statistical signiﬁcance between viterbi/prob variations for
250-character summaries. This is reasonable when considering that only a small number of strongly domain-related
utterances can be included in such short summaries and thatthe utterances given high weights by the forward-backward
algorithm are also selected by Viterbi decoding. When we focus on how the weights of words affect the results, we clearly
see that the incorporation of the domain-dependent weightfunction is effective. This fact also conﬁrms our assumptionthat having domain-related elements makes good summaries.
Overall, our new summarization method (prob/prob-DD) produced good summaries with different compression rates andthat using posterior probabilities made solid improvements.

accurately correct utterances are selected from dialogues byusing F-measure.
4. EXPERIMENT
We performed an experiment to verify our summarization
method. Using the transcripts of 391 dialogues (cf. Table 1),
we trained three types of HMMs; namely, ergodic, ergodiccs, and concat. Here, the HMM for each domain has twostates (one state each for an operator and a caller), and, forergodic-cs and concat, we set the number of common states
to six (three states each for an operator and a caller), which
led to good performance in [4]. The number of topics for
LDA was 100.
For comparison, we also prepared three variations for assigning the weights for utterances. They are ‘even’, ‘viterbi’,and ‘prob’. Here, ‘even’ gives equal weights to all utterancesand simulates the case when we apply a standard ILP-basedsummarization to all utterances in a dialogue. The second
variation, ‘viterbi’, gives 1.0 to the utterances selected by
Viterbi decoding and a very small ﬂooring value (1.0−6 ) to
unselected ones. This simulates our previous work [4] basedon Viterbi decoding. A ﬂooring value was added here to avoid
having zero weights, which can result in too short summaries.
Our third variation, ‘prob’, is our newly proposed one anduses the posterior probabilities for the weights of utterances.
Here, zero probabilities are also ﬂoored. In addition, we havetwo variations for assigning the weights for words; namely,
one that uses the TF and another that uses the weight functionof eq. (7). We call the latter variation the domain dictionary (DD). As a result, we have six variations of weight functions: (a) even-TF, (b) viterbi-TF, (c) prob-TF, (d) even-DD,(e) viterbi-DD, and (f) prob-DD.

4.2. Impact of HMM Topologies
When we look into the impact of HMM topologies, we see arather interesting phenomenon: when the common states are
introduced (i.e., ergodic-cs), the performance goes up for both‘viterbi’ and ‘prob’ variations. However, when concatenatedtraining is applied, the performance for viterbi and viterbiDD drops, whereas that for prob and prob-DD generally improves.
To investigate why this is, we made a breakdown of correctly selected utterances categorized by which HMM is responsible for selecting them in Viterbi decoding (see Fig. 4).
We found that, in concat, many correct utterances were generated from domain 0 compared to ergodic-cs. We consider that
because too many utterances were labeled as domain 0 (i.e.,having only the ﬂooring values), concat could only create itssummaries using a limited number of domain-related utterances, leading to its degraded performance. More utterances
had to be considered for inclusion. Following this result, we
reviewed our reference summaries and found that there are afair number of utterances that look common across domains,such as ‘yes’ and operators’ thanking and acknowledging;

4.1. Results
Tables 2 and 3 show the summarization results for the 240
test dialogues (transcripts) in F-measure for 250- and 500character summaries, respectively, for all variations. It can65

important words and used the forward-backward algorithm
to derive posterior probabilities that represent how likely
utterances in a dialogue belong to a particular domain andused those probabilities to represent the importance of utterances. Experimental results showed that our revised method(prob/prob-DD) successfully creates summaries of different
compression rates and outperforms existing methods, including our previous one. The improvement was signiﬁcant for
500-character summaries. By analyzing the trained models,
we also found supporting evidence that concatenated training
successfully models common utterance sequences. Our future
work includes improving the utterance selection accuracy andapplying our method to speech recognition results.

Table 4. Breakdown (in ratios) of correctly selected utterances categorized by which HMM (HMM for domain 0,
HMM for the target domain, or the HMMs for other domains)
is estimated to have generated them in Viterbi decoding.
250-character summary
domain 0 target-domain
ergodic-cs
0.384
0.411
0.491
0.372
concat500-character summary
domain 0 target-domain
ergodic-cs
0.422
0.377
concat0.522
0.343

other-domain
0.205
0.137
other-domain
0.200
0.135

Table 5. Ratio of utterances not selected by Viterbi decoding
and whose posterior probabilities remained 0 or became morethan 0 when the forward-backward algorithm is used.
ergodic
ergodic-cs
concatremained 0
0.000
0.000
0.486

6. REFERENCES[1] Hironori Takeuchi, L Venkata Subramaniam, Tetsuya Nasukawa, Shourya Roy, and Sreeram Balakrishnan,“A
conversation-mining system for gathering insights to improveagent productivity,” in Proc. CEC-EEE, 2007, pp. 465–468.

became more than 0
1.000
1.000
0.514

[2] L. Venkata Subramaniam, Tanveer A. Faruquie, Shajith Ikbal,
Shantanu Godbole, and Mukesh K. Mohania, “Business intelligence from voice of customer,” in Proc. ICDE, 2009, pp.
1391–1402.

however, such utterances were nonetheless selected by the analyst because they appear close to important utterances, suchas requests and decisions. Since concat is trained so as to discriminate common utterance sequences from domain-speciﬁcsequences as much as possible, when we use Viterbi decoding, such common-looking utterances were forcefully categorized as common (domain 0) and were not selected. In contrast, prob and prob-DD performed well with concat becausesuch utterances were given certain (i.e., more than the ﬂooring value) weights because they are common but not totally
so. The combination of prob and concat can perform suchdelicate utterance selection, leading to its good performance.
Table 5 shows the ratio of utterances that are not selected by Viterbi decoding and whose posterior probabilities
remained 0 or became more than 0 by using the forwardbackward algorithm. The table indicates that, for ergodic
and ergodic-cs, the forward-backward algorithm gave someposterior probabilities for all utterances. This is reasonablebecause the HMM for domain 0 was trained using the dataof all domains. However, for concat, about a half of the utterances still had zero posterior probabilities, which indicatesthat concat is successfully distinguishing totally common utterances from less common ones, enabling it to successfully
create summaries when used with prob and prob-DD.

[3] Roy J. Byrd, Mary S. Neff, Wilfried Teiken, Youngja
Park, Keh-Shin F. Cheng, Stephen C. Gates, and Karthik
Visweswariah, “Semi-automated logging of contact center
telephone calls,” in Proc. CIKM, 2008, pp. 133–142.
[4] Ryuichiro Higashinaka, Yasuhiro Minami, Hitoshi Nishikawa,
Kohji Dohsaka, Toyomi Meguro, Satoshi Takahashi, andGenichiro Kikui, “Learning to model domain-speciﬁc utterance sequences for extractive summarization of contact center
dialogues,” in Proc. COLING, 2010, vol. Poster, pp. 400–408.
[5] Lawrence R. Rabiner and Biing-Hwang Juang, “An introduction to hidden Markov models,” IEEE ASSP Magazine, vol. 3,
no. 1, pp. 4–16, 1986.
[6] Dan Gillick and Benoit Favre, “A scalable global model forsummarization,” in Proc. the Workshop on Integer Linear Programming for Natural Language Processing, 2009, pp. 10–18.
[7] Regina Barzilay and Lillian Lee, “Catching the drift: Probabilistic content models, with applications to generation andsummarization,” in Proc. HLT-NAACL, 2004, pp. 113–120.
[8] Toyomi Meguro, Ryuichiro Higashinaka, Kohji Dohsaka, Yasuhiro Minami, and Hideki Isozaki, “Analysis of listeningoriented dialogue for building listening agents,” in Proc. SIGDIAL, 2009, pp. 124–127.
[9] Douglas A. Reynolds, Thomas F. Quatieri, and Robert B.
Dunn, “Speaker veriﬁcation using adapted Gaussian mixture
models,” Digital Signal Processing, vol. 10, no. 1-3, pp. 19 –41, 2000.

5. CONCLUDING REMARKS[10] Kai-Fu Lee, Automatic speech recognition: the developmentof the SPHINX system, Kluwer Academic Publishers, 1989.

This paper reported improvements we made to our previously proposed HMM-based summarization method for
multi-domain contact center dialogues. For the control ofcompression rates, we formulated the summarization problem as the maximum coverage of important utterances and[11] Wen tau Yih, Joshua Goodman, Lucy Vanderwende, andHisami Suzuki, “Multi-document summarization by maximizing informative content-words,” in Proc. IJCAI, 2007, pp.
1776–1782.

66