Automatic Summarization of Multiple Travel Blog
Entries Focusing on Travelers’ Behavior
Shumpei Iinuma,
Hidetsugu Nanba, andToshiyuki Takezawa
Graduate School of Information Sciences, Hiroshima City University, Hiroshima,Japan{iinuma, nanba, takezawa}@ls.hiroshima-cu.ac.jp

Abstract
The evolution of information and communication technology now makes it possible to collect
travel information in a variety of ways. Social media content that includes blogs is one such
useful information source when planning a trip. In this study, we propose a method forgenerating a summary of multiple travel blog entries that contain images. Our method identifies
significant sentences in addition to the images by using a graph-based approach that takes
account of travelers’ types of behavior. To investigate the effectiveness of our method, we
conducted experiments, which demonstrated that our method can outperform some baseline
methods. We also implemented a system for generating summaries based on our method.
Keywords: Travel Blog; Multimedia Summarization; Travel Information Processing.

1 Introduction
Travel guidebooks are a useful information source about travel. Guidebooks givebasic information about tourist spots, souvenirs, restaurants, and hotels. However,social media content, particularly travel blogs, are another, more recent, information
source that provides many bloggers’ experiences of travel destinations, tourist spots,
and hotels.
Various researchers have investigated travel blogs as an information source for travel.
For example, Nanba et al. (2009) proposed a method that can identify travel blog
entries automatically from blogs using machine learning technology. Fujii et al.
(2016) proposed a method of classifying travel blog entries into the five categories (orcontent types) shown in Table 1. However, these methods are not useful for travel
planning if there are many travel blogs related to the intended destination, because it
would take too long to read all of them.
In this paper, we propose a method that summarizes a set of travel blog entries about adestination. Although there are many studies about text summarization, which we will
describe in Section 2.2, a notable difference between previous studies and ourapproach is that our method focuses not only on texts but also on images. Oursummarization method is based on LexRank (Erkan et al., 2004), which were
proposed for summarizing texts. Main contribution of our work is to expand the
LexRank to summarize not only texts but also images. Potential travelers using oursystem can quickly get essential information about a destination from our system’s
output summary, which contains both text and image information.
The remainder of this paper is organized as follows. Section 2 discusses related work.
Section 3 describes our method. To investigate the effectiveness of our method, weconducted experiments whose results are reported in Section 4. Section 5 shows thesystem behavior in terms of snapshots. We present some conclusions in Section 6.
Table 1. Content types and their descriptions
Content Type

Description

Watch

Blog entry about sightseeing at tourist spots

Experience

Blog entry about an experience such as scuba diving or dancing

Buy

Blog entry about shopping or souvenir stores

Dine

Blog entry about drinking and dining

Stay

Blog entry about accommodation

2 Related Work
2.1 Travel Information Recommendation
Wu et al. (2008) proposed a system that searched and summarized tourism-related
information. When a user (traveler) entered a query, such as “What is the historicalbackground of Tian Tan?” the system searched for and obtained information from
Wikipedia, Flickr, YouTube, and official tourism Web sites using the tourist spot
name as a query. Their system also classified the query as belonging to one of fivecategories, namely “general,” “history,” “landscape,” “indoor scenery,” and “outdoor
scenery,” to provide users with more relevant information. For example, if a query isclassified as belonging to the “history” category, the information is obtained fromtexts, whereas a query regarding “outdoor scenery” obtains its information fromphotos and videos. However, even if a query is classified to “history” category,showing a text with images as an answer might be easier to understand than justshowing a text. Therefore, we construct a system that can generates summaries
comprising multiple sentences and images from a set of travel blog entries.
Hao et al. (2010) proposed a method for mining location-representative knowledge
from travel blogs based on a probabilistic topic model (the Location-Topic model).
Using this model, they developed three modules, namely a destinationrecommendation module for flexible queries, a characteristics-summarization module
for a given destination (with representative tags and snippets), and an identification
module for informative parts of a travel blog that enriched the recommendations withrelated images. However, the output summaries do not always match with images,
because this system extracts them from different sources. We propose asummarization method that takes account of association between texts and images.
2.2 Text Summarization
Text summarization is a method that identifies important information in a text (ormultiple texts), and shows the results as a summary text. Text summarization has beenstudied since Luhn (1958) and has become a hot topic in the field of natural language
processing. Typically, the traditional approach to text summarization is to identify
important sentences from a text (or multiple texts) and output them as the result of thesummarization. Several methods have been proposed to identify important sentences

in texts. We now describe one such well-known method, called LexRank (Erkan et al.,2004), whose effectiveness has been confirmed in other text-summarization research,
including the Text Analysis Conference (TAC) (https://tac.nist.gov/), an evaluation
workshop for text summarization. In our work, we also adopt LexRank.
LexRank calculates the importance of each sentence based on the idea of centrality ineigenvectors and creates a similarity graph. Figure 1 is an example of such a graph.

Fig. 1. An example of a similarity graphIn the graph, each node indicates a sentence, with the number in each node identifying
the node. If the similarity between two sentences exceeds a threshold value, the two
nodes are linked, as shown by the edges in the figure. From this graph, the importanceof each node u (a sentence) is calculated using the following equation (1). Thisequation is similar to the PageRank equation (Brin and Page, 1998).
1−𝑑
𝑝(𝑣)
𝑝 𝑢 =
+𝑑
,
(1)
𝑁
deg (𝑣)
0∈234[6]where N indicates the number of nodes (sentences), d is a “dumping factor” (Brin andPage, 1998), adj[u] is the set of nodes linked to node u, and deg(v) is the order ofnode v. As for PageRank, sentences that have many links to others tend to obtainhigher page-rank scores. Erkan et al. proposed Continuous LexRank, which is anextension of LexRank. LexRank treats all links between sentences equally, whereas
Continuous LexRank employs a similarity value between sentences as a weight foreach link. Continuous LexRank is defined by equation (2).
1−𝑑
𝑝 𝑢 =
𝑁
𝑤𝑒𝑖𝑔ℎ𝑡 𝑢, 𝑣+𝑑𝑝(𝑣) (2)@∋234[0] 𝑤𝑒𝑖𝑔ℎ𝑡(𝑧, 𝑣)
0∈234[6]In our work, we expand LexRank to generate a summary that comprises a text andimages. If we can associate texts with images, we can calculate the importance ofeach sentence and image at the same time. We consider that the two sentences
appearing before and after an image are related to that image, and create one graphfrom both sentences and images. We then apply LexRank (or Continuous LexRank)
to this graph to calculate the importance of each sentence and text. In applying theequation, we also employ a “biased factor” (Otterbacher et al., 2009), which we will
describe in Section 3.

3 Summarization of Multiple Travel Blog Entries
Our system assumes that the travel blog entries to be summarized have been
geotagged and classified into five categories using Fujii’s method (Fujii et al., 2016)
in advance. When a user specifies a content type and geographical region, our systemgenerates a summary from all blog entries having the relevant content type within theregion. A summary is generated by the following procedure.
•••

Cluster the travel blog entries to be summarized,
Calculate the importance of each sentence and image for each cluster,
Select three to five important sentences and images for each cluster.

Because the blog entries to be summarized might contain multiple topics, we conducthierarchical clustering when grouping blog entries having a similar topic. We employ
the furthest-neighbor method for the clustering. In the clustering process, we merge
two clusters if the distance between these clusters is smaller than a threshold value.
Here, we express each document as a vector of words, whose weights are calculated
by tf*idf (term frequency-inverse document frequency). To calculate the distance
between clusters ci and cj, we use the function f(ci, cj) = 1–cos(ci, cj), where cos(ci, cj)
indicates cosine similarity.
3.1 Calculation of the Importance of each Sentence and Image Using LexRank
As discussed in Section 3.2, both LexRank and Continuous LexRank obtain theimportance of each sentence by calculating the PageRank score for each node of asentence-similarity graph. We expand these algorithms by calculating the importanceof each sentence-and-image together, using a graph created from sentences andimages. We consider that the two sentences appearing before and after an image are
related to that image, and create one graph from sentences and images. In creating thisconcatenation graph, we consider the similarity score between an image and its
adjacent sentences as 1. We then use the weights ai,j in equation (3) to calculate theweight between nodes i and j (sentence/image).
sim(𝑠D ,𝑠4 ) (type(𝑠D ) = type(𝑠4 ))𝑎D,4 = 1 (type(𝑠D ) ≠ type(𝑠4 ) and |𝑖 − 𝑗| = 1), (3)0 (otherwise)
where s indicates an element (a sentence or an image) in a blog-entry sentence and si
indicates the ith element in a blog entry. type(si) indicates whether each element is asentence or an image, and sim is a function that calculates the similarity between twoelements. Here, we use different similarity functions for each element type. We will
explain the calculation between elements in Section 3.2. In our example of a graph(see Figure 1), white and gray nodes indicate sentences and images, respectively. The
size of each node indicates its importance, as calculated using equation (4) in Section
3.3.
3.2 Similarity between Sentences and Images
Each sentence is expressed as a vector, where each element of the vector is the tf*idf
score for a word. We use cosine similarity for the calculation between vectors. Each
image is expressed by two kinds of vector, namely a color histogram and a bag ofvisual words (BoVW). We calculate the similarity between two images for both typesof vector, and take the average of the two similarity values as the similarity between
the two images. The color histogram is created by projecting all elements in an image
to an HSV color space divided into 160 areas (H, S, and V are divided into 10, 4, and4, respectively) and then counting the number of elements in each area. The BoVW
represents images by vectors of the frequency of appearance of local features. Thesefeatures were obtained by extracting from images using an algorithm, such as SIFT
(Lowe, 1999), and by clustering them. BoVW was originally applied in natural
language processing as a “bag of words” (BoW) that represented documents by
vectors of appearance frequency for its constituent words.
3.3 Expansion of LexRank Using Content Types
In each travel blog entry, there are sentences that have a strong relationship with agiven content type, and we expect these sentences to be in the summary. To achievethis, we employ biased LexRank, which consists of the following two steps(Otterbacher et al., 2009). First, we calculate the degree of association of eachsentence with a given content type. Second, we use these degrees to calculate theimportance of each node using the following equation, which is an extension ofequation (2).
𝑡𝑦𝑝𝑒𝑆𝑐𝑜𝑟𝑒(𝑢)𝑝 𝑢 = 1−𝑑0 𝑡𝑦𝑝𝑒𝑆𝑐𝑜𝑟𝑒(𝑣)𝑤𝑒𝑖𝑔ℎ𝑡 𝑢, 𝑣+𝑑𝑝(𝑣) (4)@∋234[0] 𝑤𝑒𝑖𝑔ℎ𝑡(𝑧, 𝑣)
0∈234[6]In equation (4), typeScore(u) indicates the degree of association of a sentence u with agiven content type. typeScore(w), as defined by equation (5), is calculated by the
degree of association of each word in a sentence u with a given content type.
𝑐𝑜𝑢𝑛𝑡 𝑤, 𝐷b
𝑡𝑦𝑝𝑒𝑆𝑐𝑜𝑟𝑒 𝑤 = 𝐼𝐺 𝑤, 𝑡 log 1 +(5)𝑐𝑜𝑢𝑛𝑡 𝑤, 𝐷
Here, IG(w, t) indicates information gain of word w for a content type t. count(w, D)
indicates the number of blog entries that contain the word w in the set of travel blog
entries D. Dt is the number of travel blog entries associated with content type t. If theword w often appears only in travel blog entries with content type t, values of bothIG(w, t) and count(w, Dt) will become large, and as a result, typeScore(w) will also
become large. Now, we consider typeScore(w), which has the highest value among allwords in sentence u, as typeScore(u).
3.4 Reduction of Redundancy
After applying LexRank to a sentence-similarity graph, there might be sentences
having high importance scores that are very similar to each other. As a result, there
would be redundancy in the summary generated. To resolve this problem, Radev et al.
(2000) reranked sentences by taking account of their containment relationship. In thesame way, our method rejects sentences and images that are sufficiently similar (theirsimilarity values exceed a threshold value) to sentences or images that have alreadybeen chosen as part of the summary.

4 Experiments
4.1 Experimental Settings
We used the travel blog entries collected using Nanbas’ method (Nanba et al., 2009),
and the content types were assigned using Fujiis’ method (Fujii et al., 2016). These
entries and their content types were manually checked. We selected 20 areas in Japan,
and chose approximately 10 blog entries for each area. We then created correctsummaries manually by choosing three images and five sentences for each content
type. Finally, we obtained 47 human-produced summaries, which we used in theevaluation of our system.
4.1.1

Evaluation

We conducted both automatic and manual evaluations.
Automatic evaluation:
For the evaluation of the text part of computer-produced summaries, we employed
ROUGE-N (Lin, 2004), which is widely used as an evaluation metric in textsummarization research and projects such as TAC (https://tac.nist.gov). ROUGE-N is
calculated by dividing the number of N-word-grams that are contained in bothhuman-produced and computer-produced summaries by the number of word N-gramsin a human-produced summary. That is, ROUGE-N is a metric of how well acomputer-produced summary covers the word N-grams in a human-produced
summary. For the values of N, we used N = 1 and N = 2.
For the automatic evaluation of the image part of computer-produced summaries, weused recall and precision as evaluation metrics. We asked human subjects to choose
representative images for each topic, and used them for the evaluation of the image
part of the computer-produced summaries.
Manual evaluation:
We evaluated computer-produced summaries manually from the following two
viewpoints:ll

Are the essential points of blog entries contained in the computer-produced
summary? (MANUAL-TEXT).
Do the images and texts in each computer-produced summary match?
(MANUAL-IMAGE-TEXT).

In this evaluation, we asked human subjects to evaluate summaries according to thefollowing procedure.
1.
2.
3.

Read all blog entries to be summarized.
Read a human-produced summary and five computer-produced summaries (tobe described later), evaluating in terms of a five-point scale (MANUALIMAGE-TEXT).
Rank six summaries (MANUAL-TEXT). (This ranking-based evaluation was
employed in the evaluation workshop NTCIR-2 Text Summarization Challenge(Fukushima et al., 2002).) If the qualities of two summaries were considered thesame, we allowed the human subjects to rank these summaries equally.

Alternatives:
We performed evaluations for the following five methods. For each method, we
adjusted the length of the summary and the number of images to be the same as thehuman-produced summaries.
l
l
l
l
l

Lead (baseline): extract sentences and images from the head of each blog entry.
LR (baseline): construct a sentence similarity graph and an image similarity
graph, and then apply LexRank to each graph (equation (2)).
LR+IMG (our method): construct one graph by connecting the sentence
similarity and image similarity graphs, and apply LexRank (equations (2) and(3)).
LR+TYPE (our method): apply LexRank, while taking account of content
types (equation (4))
LR+IMG+TYPE (our method): apply the LR+TYPE method to a similarity
graph in the LR+IMG method.

In conducting hierarchical clustering, we used 0.9 as a threshold value for merging
two clusters (see Section 3). We standardized on 16 pixels, and sampled every 8
pixels to obtain the SIFT features. Here, SIFT is an algorithm to detect and describe
local features in images (Lowe, 1999). We then calculated the BoVW vectors byconducting K-means clustering, fixing 1,000 for the cluster numbers (see Section 3.2).
For the extraction of SIFT features, we used OpenCV software (http://opencv.jp). We
employed 0.85 as the dumping factor for LexRank (equation 2). To reduce theredundancy in computer-produced summaries, we used threshold values of cosine
distance = 0.9 for sentences and 0.5 for images (see Section 3.4).
We obtained content-type-related words using 1,836 travel blog entries (purchase:
147, watch: 1,145, experience: 119, stay: 38, and dine: 693). None of these travel blog
entries included entries that were used in the summarization.
4.2 Results and Discussion
The evaluation results using ROUGE (automatic evaluation of text parts) are shownin Table 2. Note that both LR+TYPE and LR+IMG+TYPE outperformed two of thebaseline methods. We conducted a t-test, which confirmed a significant difference
between our methods and these baseline methods.
Table 2. Evaluation results using ROUGE-N (automatic evaluation of text parts)
ROUGE-1

ROUGE-2

Lead (baseline)

0.318

0.222

LR (baseline)

0.316

0.207

LR+IMG

0.331

0.227

LR+TYPE

0.345

0.240 Highest values among

LR+IMG+TYPE

0.340

0.237 in bold.

all systems are shown

We show another set of evaluation results (automatic evaluation of image parts) in
Table 3. Here, our LR+IMG+TYPE method outperformed other methods, but wecould not confirm the statistical significance of this result.

Table 3. Evaluation results using precision/recall (automatic evaluation of image
parts)
Precision

RecallLead (baseline)

0.351

0.359

LR (baseline)

0.341

0.341

LR+IMG

0.338

0.342

LR+TYPE

0.351
0.372

0.359 Highest values among
all systems are shown
0.367 in bold.

LR+IMG+TYPE

We show the results for manual evaluation in Tables 4 and 5. Table 4 gives theaverage rank of each summary by two human subjects, where a smaller average rank
indicates a better summarization. Table 5 gives a five-point scale values of eachmethod, where a larger value indicates a better summarization. We conducted a t-test(p < 0.05), which confirmed that there was a significant difference between our
methods (LR+IMG, LR+TYPE, and LR+IMG+TYPE) and the Lead method.
Table 4. Evaluation results by human subjects (MANUAL-TEXT)
Average rank
Human-produced summaries
Lead (baseline)
LR (baseline)
LR+IMG
LR+TYPE
LR+IMG+TYPE

1.28
4.01
3.09
2.85
3.22 The highest value
among all systems is
2.99 shown in bold.

Table 5. Evaluation results by human subjects (MANUAL-IMAGE-TEXT)
Average rank
Human-produced summaries

4.33

Lead (baseline)

2.80

LR (baseline)

3.09

LR+IMG

3.12

LR+TYPE
LR+IMG+TYPE

2.96
3.05

The highest value
among all systems isshown in bold.

Next, we compared our methods with LexRank (LR) for each summarization type.
Tables 6 and 7 show the number of cases for which our methods are better/worse thanLR for each content type in TEXT and IMAGE-TEXT evaluations, respectively. For
example, “LR < LR+IMG” indicates that lR+IMG outperformed LR, and the shaded
cells indicate where our method outperformed or matched LR. Table 6 shows that our
LR+IMG method outperformed or matched LR for all content types. However,
LR+TYPE could not match LR for any content type. Table 7 shows that all of our
methods outperformed or matched LR for content type “Watch,” whereas our
methods could not match LR for content type “DINE.” Through further investigation,
we found that these results relate to the amount of text associated with each image.

Table 6. Comparison of LexRank (LR) with our methods for each content type
(MANUAL-TEXT)
Watch

Dine

Exp., Buy, Stay

Total

LR < LR+IMG
LR > LR+IMG
LR = LR+IMG

12
7
23

12
6
12

4
3
15

28
16
50

LR < LR+TYPE
LR > LR+ TYPE
LR = LR+TYPE

7
14
21

9
9
12

2
6
14

18
29
47

LR < LR+IMG+TYPE
LR > LR+IMG+TYPE
LR = LR+IMG+TYPE

13
13
16

15
11
4

7
6
9

35
30
29

Table 7. Comparison of LexRank (LR) with our methods for each content type
(MANUAL-IMAGE-TEXT)
Watch
LR < LR+IMG

Dine

Exp., Buy, Stay

8

10LR > LR+IMG

5

LR = LR+IMG

29

LR < LR+TYPE

Total

4

22

10

3

18

10

15

54

6

6

2

14

LR > LR+ TYPE

4

11

7

22

LR = LR+TYPE

32

13

13

58

LR < LR+IMG+TYPE

12

9

6

27

LR > LR+IMG+TYPE

9

12

6

27

LR = LR+IMG+TYPE

21

9

10

40

Table 8. Comparison of LexRank (LR) with our methods for each content type, interms of the number of characters associated with each image
Less than 100 characters

Over 100 characters

LR < LR+IMG

18

10LR > LR+IMG

10

6

LR = LR+IMG

30

20

LR < LR+TYPE

11

7

LR > LR+ TYPE

20

9LR = LR+TYPE

27

20

LR < LR+IMG+TYPE

21

14

LR > LR+IMG+TYPE

22

8

LR = LR+IMG+TYPE

15

14

Using the same approach, we investigated the relationship between the amounts oftext associated with images and the quality of summaries. Table 8 gives the results ofa comparison between the LR method and our methods for different numbers ofcharacters per image. From Table 8, we see that only the LR+IMG method can matchthe LR method when the number of characters is less than 100, whereas both the
LR+IMG and LR+IMG+TYPE methods outperform the LR method when the numberof characters is more than 100.

5 System Behavior
In this section, we introduce our system’s behavior in terms of the travel blog entries
collected and classified by the Nanbas’ method (Nanba et al., 2009) and the Fujiis’
method (Fujii et al., 2016), which were mentioned in the previous section. Figure 2
shows a map that summarizes multiple travel blog entries, as generated by our system.
In this figure, blog entries are shown as icons. If we push one of the buttons “watch,”“experience,” “purchase,” “dine,” or “stay” (as listed in Table 1), the blog entries
corresponding to this category are shown on the map. Clicking an icon on the map
produces a list of the blog entries related to that point.
After clicking a button on the bottom left of the map, a computer-generated summary
appears in a pop-up window, which summarizes all the blog entries shown in the map.
The blog entries shown in the map will refer to the particular content type that thesystem user has specified. If the user chooses a different content type, the system willquickly generate another summary from the travel blogs related to that content type.
Figures 3 and 4 are the computer-generated summaries for content types “watch” and“dine”, respectively. Although both summaries were generated for the same location,
namely “Miyajima” (one of the most famous tourist spots in Japan), the summaries
refer to different aspects of a visit to Miyajima.

watch buy stay dine experienceFig. 2. Travel blog entries in a map for the content type “watch”

6 Conclusions
We considered that travel blog entries are useful information source for travel,
because, they provide many bloggers’ experiences of travel destinations. Therefore,
we have proposed a method for summarizing multiple travel blog entries. Our method
is an extension of LexRank to enable generation of a summary text containing images.

We conducted experiments that demonstrated that one of our methods, LR+IMG, canoutperform baseline methods. Finally, we constructed a summarization system thatcan summarize multiple travel blog entries in terms of content types for any given
geographical region. The system is available on our web site(http://165.242.101.30/blogMap/). Our system can generate a summary very quickly
if the number of travel blog entries are less than 100. However, when the number of
entries is over 100, our system will not generate a summary, because it is quite timeconsuming. How to decrease the processing time is our future work.
Ø
Ø
Ø

Ø

Ø
Ø

This is a firework festival that I
wanted to watch before I moved toHiroshima.
I went to Miyajima firework festival
on August 14.
My proposal was rejected by myhusband and my daughter, andfinally we watched firework on aship.
Miyajima island is known as one ofthree most scenic spots in Japan,
Itsukushima shrine, and world
heritage.
This is a shrine in Itsukushima(Miyajima island) in Hiroshima.
In future, we might know the detailof Miyajima a thousand years ago.

Fig. 3. A summary generated from travel blogs on Miyajima for the content type “watch”

Ø
Ø
Ø

Ø
Ø
Ø

Last February, our family attended
Miyajima oyster festival.
We went to “the fried oyster” line.
We could eat grilled oyster (free),oyster with rice, fried oyster, udon
noodle with oyster, and fresh
seafood.
We arrived at Miyajima port, but it
does not look like a port.
We arrived at Miyajima island byferry. This island is famous for aworld heritage, Itsukushima shrine.
We arrived at Miyajima at 11:00 am.

Fig. 4. A summary generated from travel blogs on Miyajima for the content type “dine”

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