Generating Experiential Descriptions andEstimating Evidence Using Generative
Language Model and User Products Reviews
Shen Chenfu

Yoshiyuki Shoji

Takehiro Yamamoto

Graduate School of Science and Engineering
Aoyama Gakuin University
Kanagawa, Japan
shen@sw.it.aoyama.ac.jp

Faculty of Informatics
Shizuoka University
Shizuoka, Japanshojiy@inf.shizuoka.ac.jp

School of Social Information Science
University of Hyogo
Kobe, Japant.yamamoto@sis.u-hyogo.ac.jp

Katsumi Tanaka

Martin J. Dürst

Faculty of Informatics
The University of Fukuchiyama
Kyoto, Japantanaka-katsumi@fukuchiyama.ac.jp

College of Science and Engineering
Aoyama Gakuin University
Kanagawa, Japanduerst@it.aoyama.ac.jp

Abstract—This paper introduces a method to transform technical product descriptions into user-friendly experiential descriptions, while also highlighting relevant parts of the originaldescription. Product descriptions often are hard to understandwithout prior knowledge. For example, a beginner with a camera
cannot understand technical descriptions like “ISO sensitivity
51,200”. Our method translated this description to more relatablephrases such as “captures clear faces even at night.” Our method
adopts a generative language model to enable such experiential
description generation and evidence estimation. Our method
first trains a model with pairs of product descriptions and reviews. The trained model generates many candidate experiential
descriptions when given product descriptions. After training,
our method uses an ablation-based approach to estimate theevident description of the generated candidates. It checks for thefrequency of words in the generated narrative when a portion of
the description is removed. For example, terms like “night” or“clear” became less prevalent in reviews when “ISO sensitivity”
was removed from the input description. Subject experimentswith the actual review dataset verified our method’s effectivenessin generating accurate narratives highlighting product features.
Index Terms—Generative Language Model, Catchphrase, Slogan

I. I NTRODUCTION
In recent years, people who have never shopped online
might be in the minority. The proliferation of online shoppingsites and the rise of social commerce platforms like eBay
have facilitated casual Internet trading. People of all ages andgenders increasingly select and purchase online products. In
this context, the importance of finding suitable products fromthe vast array available on online shopping sites has grown.
Typically, when seeking a product for a particular purpose,many people read the product description. However, productdescriptions on online shopping sites often require domainspecific knowledge for comprehension. For instance, a novice

who wishes to purchase a camera from an online site thatcan beautifully capture birds. Generally, camera product pages
feature specification information such as “Image Sensor: APSC” or “ISO 25,600”. For people familiar with cameras, these
specifications serve as valuable decision-making references.
Yet, for novices without prior knowledge, understanding theimplications of these specifications proves challenging, making it hard to determine if the camera suits the purpose of
capturing birds.
In such cases, user-submitted product reviews act as additional sources of information to aid product selection. If a
review mentions that the camera can capture birds beautifully,
even novices can discern that the camera might be suitable for
capturing distant, small, and moving subjects. Such experiential descriptions by purchasers assist many novices in judging
the product’s utility and making purchase decisions.
However, not all cameras suitable for bird photography
necessarily feature a review stating their capability to capturebirds beautifully. And if multiple cameras bear such reviews,
deciding on the best option becomes complex. One must
understand experiential descriptions and product descriptions
to make informed decisions. Specifically, customers must siftthrough numerous reviews and product descriptions to deduce
relationships like “Products often reviewed as capable ofbeautifully capturing birds tend to have high zoom capabilities
and fast shutter speeds.” Grasping the relation between productfeatures and their results significantly burdens novices.
This research introduces an algorithm that, when fed a
product description, outputs potential experiential descriptions
from using the product and indicates the parts of the productdescription that serve as the basis for these narratives. Figure
1 illustrates a sample input-output scenario for the proposed
application. The algorithm accepts any product’s description.

InputFull text of arbitrary
product description
This is a mirrorless SLR camera.
Full size sensor with ISO sensitivityof 204800. 16-point autofocus.
Zoom lens has a maximum focal
length of 300mm. 16.4Wh battery.
Flash function.
Plain text of a product description

OutputRanked list of experiential
descriptions, and Evidence(a part of product description)

1. Good for photograph birds in the distance.
(Evidence: focal length 300mm)

2. Faces can be clearly photographed even at night.
(Evidence: ISO 204800)

3. Suitable for watching soccer games.
(Evidence: focal length 300mm)

4. Ideal for traveling.

(Evidence: Battery 16.4Wh)

Fig. 1. Expected Inputs and Outputs. When a product description is entered,an experiential description tailored to its characteristics is output. Evidence isassigned to each description.

It then auto-generates multiple reviews likely to associatewith the product’s features. For each generated review, thesystem determines and indicates the specific feature in the
product description that led to that narrative. For instance,
a specialized term like “ISO sensitivity 409,600” can betranslated into an experiential description such as “Can capture
clear faces even at night,” followed by the justification “Basedon: ISO sensitivity 409,600”. Even novices can expect amore straightforward product selection process by juxtaposing
experiential descriptions and their justifications.
This study employed generative language models pretrained on extensive corpora to convert product descriptions
into experiential descriptions. This approach was adopted to:• Understand the vague and diverse expressions found inuser reviews,• Facilitate natural language inference, and• Cater to products that lack reviews.
The structure of this paper is as follows: The paper consistsof a total of six sections. In this section, the background andmotivations leading to this research have been discussed. In
section II, related works about this research are introduced, andthe positioning of this study is clarified. Section III describes
the specific methodology proposed in this research. Section
IV elaborates on the evaluation of the proposed methodology.
In Section V, the results of the experiments are discussed.
Section VI concludes this method and evaluation result, andshows future work.
II. R ELATED W ORKThis research relates to three areas: studies utilizing user
reviews, studies on generating recommendation phrases, andstudies presenting reasons for recommendations. In sections
II-A, II-B, and II-C, relevant studies in each area are introduced and discussed, respectively.
A. Studies Utilizing User Reviews
Recently, methods recommending items based on user reviews are becoming more common. For example, Zheng et al.
[1] propose a deep learning model that jointly learns productor service attributes and user behaviors from user reviews to
make recommendations.
B. Studies on Generating Recommendation Phrases
In this research, we generate experiential descriptions fromproduct explanations. Studies have been done on automaticallygenerating recommendation phrases in fields such as information recommendation. For example, Zhang et al. [2] report
the results of introducing an Automatic Product Copywriting
Generation system (APCG) using a machine learning model on
the JD.com product recommendation platform, generating 2.53
million product descriptions over seven months. This researchshows the feasibility and effectiveness of recommendationphrases using machine learning.
Additionally, Zhang et al. [3] generated recommendationphrases by inputting product descriptions based on a Selflabeling Conditional Variational Autoencoder (SLCVAE). Li
et al. [4] proposed a Neural Template (NETE) description generation framework to balance sentence quality and expressiveness in recommendation systems using a generative approach.
Chan et al. [5] proposed a machine learning model called SMG Net for creating product descriptions and advertisements
spanning multiple products. Deng et al. [6] proposed SGSPAC, a system for automatically personalizing advertisement
content according to consumer needs. While these studies aim
to generate product advertisements or catchphrases, our study
aims to make it easier for beginners to select products bypresenting both the phrase and its rationale.
C. Studies on Presenting Reasons for Recommendations
This section discusses studies on presenting rationales forrecommendations and explainability in machine learning models. Recently, the research area known as “Explainable AI,”
which explains the reasoning of machine learning models, israpidly growing [7]. Especially in fields like product descriptions, as in this research, studies aiming to clarify reasons
for recommendations and ensure trustworthiness are becoming
active [8].
Zhang et al. [9] proposed an Explicit Factor Model (EFM)
that presents both recommended and non-recommended products according to user interests based on product features anduser tendencies and provides reasons for why a product is or
isn’t recommended.
Sinha et al. [4] investigated user perceptions across fivemusic recommendation systems and found that users have afavorable view of recommendations when they feel there ishigh transparency in their reasons. This study indicates theeffectiveness of presenting reasons during recommendations.
III. M ETHOD
Our method accepts product descriptions as input andoutputs several experiential descriptions highlighting the product’s features. Simultaneously, it provides the portion of the
product description that serves as the basis for each description. To generate these experiential descriptions, a method
for fine-tuning the pre-trained language model, GPT-2, isTraining

Camera A 70
￥240,000

Input Product description [SEP]Buy

Product Description

Review text

Description

This camera has ISO 204,800 sensitivity, andGeneration
Input

This camera has ISO 204,800 sensitivity, with

D60 has ISO 1,600 sensitivity. F-mount.

Mirrorless with Full-frame DSLR. [SEP] I use

self-timer [SEP]693 phase-diﬀerence detection auto focus.

It has 10.75 million image pixel, and built-in

this for bird photographing.

Blank

693 point auto focus function. It also has eye
detection AF. The high-speed shutter speed

GPT-2

allows for 10 pictures per second. It has
full-size mirrorless sensor.

Reviews★★★★★
I use this camera to shoot wild

birds! Suitable for far fast things!

Convert trained
model

Review

Sentence★★★★★
It was a beautiful nighttime
factory shoot.

Fig. 2. An example of training data consists of a product description andreviews.

proposed. Furthermore, a method based on ablation is also
introduced to estimate the foundation for the generated descriptions.
A. Cleansing and Preprocessing of Product and Review Data
This method utilizes product descriptions and their associated user reviews as datasets (illustrated in Figure 2). Since thereview data consists of general user submissions, the content
varies in quality; some might not be in proper Japanese, whileothers may be irrelevant to the product. Thus, data cleansing
was necessary for preparing the learning data. Specifically,
product descriptions and user reviews underwent rule-based
data removal. A blacklist of words was created to eliminate sentences containing specific terms. Product descriptions
often include information irrelevant to the product, such as
promotions and shipping costs. Consequently, a manual listof terms frequently used in sales or shop information was
created, encompassing words like “special price,” “shipping,”and “shipping fee.” Reviews containing these terms were
removed from product descriptions.
B. Fine-tuning using Product Descriptions and User Reviews
A language model that generates multiple potential reviews
for a given product description was developed using GPT-2.
Although GPT-2 is a general-purpose large-scale generative
language model, publicly available models typically predictand produce sentence continuations. Adjustments were madeto input product descriptions and output reviews by fine-tuning
such models.
The data structure and tasks during actual training are illustrated on the left side of Figure 2. For learning purposes, pairsconsisting of product descriptions and corresponding written

Product description [SEP]Blank

The sentence that comes after

This camera has ISO 204,800 sensitivityis
The sentence that comes after
Full-frame DSLR. [SEP]iswith 693

I use

Train model with language

modeling task which estimate
the next token.

GPT-2

Output Product description [SEP]Generated Review

D60 has ISO 1,600 sensitivity. F-mount.

It has 10.75 million image pixel, and built-in
self-timer [SEP] The camera is great for

daytime, but not for night scenes. It is fun touse a variety of lenses!

Generate a review text as astatement that may follow [SEP].

Fig. 3. Example of actual input/output text for the training and generation
phases. Given a product description, the model generates a review likely
written for it.

reviews were extracted. In cases where multiple reviews were
written for a single product, they were kept separate, resulting
in numerous pairs with the same product description but
different reviews. Subsequently, the product description and its
review were connected using a unique token. Specifically, thecommonly used [SEP] separator token was inserted between
the product description and the review, combining them into
one sentence. By training with such data, GPT-2 learns thatpre-[SEP] content tends to be product-description-like, while
post-[SEP] content leans towards review-like sentences. In
this manner, a language model was created that generates areview as a continuation of the input text when provided witha product description and [SEP].
C. Generating Experiential Descriptions Using Fine-Tuned
GPT-2
Experiential descriptions are generated using the fine-tuned
GPT-2 language model (as shown on the right side of Figure3). Providing the model with any product description followed
by [SEP] produces a review as a continuation of the given
product description. The generated reviews vary in length,from one-line or one-sentence descriptions to several lines.
For this study, the generated reviews were split into individual
sentences, and those ranging from 15 to 70 characters wereextracted for experiential descriptions.
D. Estimation and Ranking of the Basis in the Product Description
Our method ascertains which portion of the product description each generated experiential description pertains to.
For this purpose, a method based on the Ablation Study,

Original Product Description
D60 has ISO 1,600 sensitivity. F-mount.

It has 10.75 million image pixel, and built-in
Remove the ﬁrst

Description

sentence

self-timer

Remove theRemove the third
sentence, ...

second sentence
F-mount.

It has 10.75 million image pixel, and built-in
self-timer [SEP]

D60 has ISO 1,600 sensitivity.

D60 has ISO 1,600 sensitivity. F-mount.

self-timer [SEP]self-timer [SEP]

It has 10.75 million image pixel, and built-in

and built-in

Review generation by ﬁnetuned model
Review

Generate Reviews for eachpartially removed descriptions

It is fun to use a variety of lenses! Fine details

This camera is not suitable for night scenes.I

The night pictures becomes grainy. Take a

I use it with a pancake lens. I use it to take

This camera is suitable for daytime

I use it with a ﬁsheye lens. It is also useful for

may tend to be blurry.

group photos!

use it for selﬁes!

photography. Suitable for landscapes, etc.

group photo with a zoom lens.

taking selﬁes.

The word "lens" does not appear when the sentence about ISO sensitivity.

Fig. 4. Overview of ablation-based method

widely employed in machine learning, was utilized. In ablation, certain features are removed during training to determine
their contribution to the machine learning output, comparedwith when these features are not removed. Following thismethodology, a portion of the description was concealed, andthe frequency of words in the generated review was used
to determine which description influences which experiential
statement. The experiential descriptions were then ranked
based on the confidence of their bases.
The outline of the rationale estimation method based on
ablation is depicted in Figure 4. First, one sentence is removed
from the product description, which consists of multiple
sentences. Then, the trained model is prompted to generate
1,000 experiential descriptions using the remaining part of the
product description as input. Next, only the nouns that appear
within the set of the 1,000 generated experiential descriptions
are extracted. By doing this, words that no longer appear in thegenerated reviews when a specific sentence is removed fromthe input are aggregated based on their appearance probability.
For instance, when a description related to “shutter speed” isomitted from the product description, there’s a tendency forreviews containing terms like “sports” or “without blur” to begenerated less frequently. In this manner, the word appearance
probability for each sentence in the description is calculated byshifting which section of the product description is removed.
Since the data used in the experiment is in Japanese, words
are not separated by spaces. Therefore, a morphological analyzer was used to split the sentences into words and estimate
their parts of speech. For this purpose, MeCab was used as themorphological analyzer. Additionally, because reviews containmany colloquial expressions and proper nouns, MeCab-ipadicNEologd, a morphological dictionary with many new words
and specialized terms, was used.
In the next step, the experiential descriptions created in
Section III-C are ranked using their probability of occurrence.
This study aims to produce experiential descriptions withhigh accuracy in their basis estimation. Thus, regardless of
the likelihood of the description being generated (i.e., its
plausibility) or whether it reflects the product’s merits, theranking is based solely on the credibility of its basis.
We compile the differences in occurrence probabilities ofwords in a generated review to determine which sentence,
when hidden, makes it less likely to produce a given word.
From this data, irrespective of the group of experiential
descriptions generated from different product descriptions,the top five words with the most significant differences inoccurrence probabilities are extracted. Note which productdescription sentences were removed when these words wereextracted. Then, those containing these words are extracted
from the experiential descriptions generated using the fullproduct description (with no sentences removed). Ultimately,
these extracted experiential descriptions can be obtained ashaving their basis in the noted sentences of the productdescription. An explanation is provided on why the final
extracted experiential descriptions can be considered to havetheir basis in the noted sentences of the product description.
For instance, when generating an experiential description
from a product description by removing only one sentenceli , assume the occurrence probability of a word w decreases
compared to when another sentence lj is removed. Thisimplies that the experiential description containing the word
w correlates with the sentence li in the product description,and thus, it can be considered as its basis.
E. Presentation of Experiential Descriptions and the Corresponding Basis within Product Descriptions
This section presents the experiential descriptions and theircorresponding basis in the product descriptions, as identified in
Section III-D. The number of experiential descriptions within
the group obtained in Section III-D is not fixed. Therefore,
in this study, a single description is randomly selected fromthis group and presented along with the portion of the productdescription that serves as its basis. Specifically, five sets ofwords have the most significant difference in occurrence probability and their corresponding basis in the product descriptions. For each set, one complementary experiential description
is presented. Thus, five experiential descriptions and theircorresponding portions of product descriptions are presented.
IV. E VALUATIONIn this section, we discuss the dataset used to demonstrate
the usefulness of the proposed method, details of the evaluation experiment conducted using this dataset, and its results.
An experiment involving human participants was conductedin this study to evaluate the efficacy of the proposed method.
A. Dataset
To create training data for fine-tuning the GPT-2 model,
product information and reviews from Rakuten Ichiba, aprimary Japanese online shopping site, were used. In theexperiments, we evaluated four product categories: earphones
and cameras, where catalog specifications are essential; and Tshirts and bread, where product descriptions from the catalog
may not be considered reliable.

Product descriptions and reviews of products with at least
one review were extracted for data extraction and preprocessing. Given the token limitation of 512 tokens, productdescriptions were truncated to 320 tokens, and review texts
were truncated to 192 tokens. These were then concatenatedusing the [SEP] token to form the training data. Regarding
dataset size, there were approximately 13,000 sets of productdescriptions and reviews for earphones and around 3,500 sets
for cameras.
B. Implementation
The GPT-2 model used in this study is a large-scale Japanese
language model, which Rinna made open-source. This model
was trained over about a month using 70 gigabytes of Japanese
text from the CC-100 dataset, ensuring its versatility. The GPT2 model was then further trained using the dataset describedin Section IV-A.
C. Comparison Methods
In this section, we describe the methods compared with theproposed method to investigate its effectiveness. To evaluate
the proposed method, we compare it with methods that use
different approaches during fine-tuning: an approach based on
co-occurrence frequency, a method that does not present any
justification from the product description, and a method that
only shows the original product description as a reference.
Specifically:• Proposed Method: The method proposed in this study
generates experiential descriptions using GPT-2 finetuned with both product descriptions and review texts.
• Review-only Training: A method using GPT-2 finetuned exclusively with product review texts.
• No Justification Presentation: A method that presents
the experiential description generated by GPT-2 without
estimating the justification from the product description,
displaying the description with the top five most frequentwords from the generated experiential descriptions.
• Co-occurrence Frequency (Baseline): A method thatcomputes word sets co-occurring in product descriptions
and corresponding reviews. The method then ranks themby the frequency of co-occurrence and extracts actual
review sentences from the dataset containing words cooccurring with words in the given product description.
The extracted sentence from the product becomes thejustification.
• Only Product Description Presentation: A method that
only displays the unaltered product description without
any experiential description.
The baseline method based on co-occurrence frequency extracts potential justifications from the product description without any generation, resulting in one sentence from the reviews.
An experiment also included GPT-2 trained only on review
texts. This is because, in product reviews, specifications often
mentioned in product descriptions can also appear. Therefore,
even without introducing on product descriptions, if a productdescription is given to GPT-2 trained to continue reviews, it

might generate experiential descriptions corresponding to thespecifications. These methods were then compared with theproposed method.
D. Experimental Method
This section presents the experimental method used to evaluate the experiential descriptions and their justification fromthe product descriptions generated by the proposed method.
An evaluation was conducted using a human subject experiment. Three participants evaluated the experiential descriptions
in a survey format. These descriptions were listed in advancefor the participants to read. The actual product and experiential
descriptions generated from the evaluation were provided tothe participants during the evaluation.
The participants were evaluated based on the following six
criteria:
1) Naturalness: Whether the experiential description is innatural Japanese,2) Experientialness: Whether the experiential description
seems genuinely experiential,
3) Correctness: Whether the content of the experiential
description is correct,
4) Interestingness: Whether reading the experiential description and its justification in the product description
sparked interest in the product,
5) Helpfulness: Whether understanding of the productdeepened after seeing the experiential description andits justification in the product description,6) Evidentness: Whether the correct part of the productdescription was presented as the justification for theexperiential description.
Each criterion was evaluated using a 5-point Likert scale.
Among these criteria, the sixth one, concerning whether thecorrect section of the product description was given as justification, might be challenging to assess without deep knowledgeof the product domain. Therefore, for this particular criterion,
an additional expert from each domain (a total of two experts)
was recruited to evaluate.
E. Experimental Setup
For the experiment, 15 product descriptions were preparedfor each product domain, totaling 60 product descriptions. For
every product description, five experiential descriptions andtheir respective justifications from the product description were
presented for each method. It is worth noting the differencesin presentation depending on the method:• For the “Product Description Only” method, neither theexperiential description nor the justification from theproduct description was presented.
• For the “Without Justification” method, only the experiential description was shown.
• For all other methods, pairs of experiential descriptions
and their justifications from the product descriptions were
presented.
To ensure a blind evaluation, the presentations were randomly shuffled so the participants could not determine whichTABLE I
AVERAGE RATINGS FOR BOTH EARPHONES AND CAMERA PRODUCT CATEGORIES ( OUT OF 5; ** p < 0.01, * p < 0.05)
Evaluation Item

Proposed Method

Review Learning

Co-occurrence

Without Justification

Naturalness
Experientialness
Correctness
Interestingness
Helpfulness
Evidentness**4.36
4.20**3.66**3.90**3.80**3.394.25**4.04**3.86**3.87**3.86**3.69

4.18
4.32
3.45
3.58
3.61
3.00

*4.01
**3.96
3.40**3.23**3.16

Product Description

3.80
3.77

TABLE IIAVERAGE RATINGS FOR EARPHONES PRODUCT CATEGORY ( OUT OF 5; COMPARED TO C O - OCCURRENCE ** p < 0.01, * p < 0.05)
Evaluation Item

Proposed Method

Review Learning

Co-occurrence

Without Justification

Naturalness
Experientialness
Correctness
Interestingness
Helpfulness
Evidentness

*4.35
4.35
*3.59
**3.93
*3.77
3.23

4.18
*4.12
**3.83**3.88**3.88**3.70

4.16
4.34
3.40
3.52
3.56
3.04

**3.81**3.82
3.33**3.11**3.09Product Description

3.78
3.73

TABLE IIIAVERAGE RATINGS FOR THE CAMERA PRODUCT CATEGORY ( OUT OF 5, COMPARED TO C O - OCCURRENCE ** p < 0.01, * p < 0.05)
Evaluation Item

Proposed Method

Review Learning

Co-occurrence

Without Justification

Naturalness
Experientialness
Correctness
Interestingness
Helpfulness
Evidentness

4.36
*4.06
*3.73
*3.88
3.82
**3.544.32**3.96**3.88*3.87
3.84**3.67

4.20
4.30
3.51
3.64
3.65
2.96

4.20
4.11
3.47
*3.36
**3.24

Product Description

3.82
3.80

TABLE IVAVERAGE RATINGS FOR T- SHIRT PRODUCT CATEGORY ( COMPARED TO C O - OCCURRENCE ** p < 0.01, * p < 0.05)
Evaluation Item

Proposed Method

Review Learning

Co-occurrence

Without Justification

Naturalness
Experientialness
Correctness
Interestingness
Helpfulness
Evidentness

4.56
4.29
**3.44**3.72
3.50**3.42**4.32**4.13**3.43*3.64
3.62**3.54

4.67
4.44
2.91
3.40
3.62
2.71

**4.46
*4.24
2.95
3.31
**3.28method produced which output. Each participant read theexperiential descriptions and justifications for every productand evaluated them based on the six criteria. In cases where amethod did not present either the experiential or product description, the corresponding evaluation criteria were omitted.
This experimental setup was meticulously crafted to comprehensively evaluate each method’s outputs without introducing biases. The design allows for a clear comparison andan understanding of the strengths and weaknesses of eachapproach.
F. Experimental Results
This section presents the results of the subject experiments
described in Section IV-D. A t-test was performed to compare
the proposed method with the co-occurrence-based method.
Items with a statistically significant difference are denoted withan asterisk (*).
The results, which compile data from the earphone andcamera product categories, are shown in Table I. The results exclusive to the earphone and camera categories are is
depicted in Table II and III, respectively. In these domains,

Product Description

*3.80
3.87

where catalog specifications are crucial, the proposed method
demonstrated a significantly higher accuracy concerning thenaturalness of the Japanese language, correctness, understanding of the product, and purchase desire than the method thatmerely extracts relevant sections.
Similarly, the T-shirt product category results, where catalog information isn’t as vital, are shown in Table IV. The
comparison concerning bread is provided in Table V. Basedon the experimental outcomes, significant differences wereobserved across all product domains in multiple evaluation
metrics, including the level of interest in the product, whencomparing the proposed method to the baseline. Moreover, theapproach utilizing GPT-2 trained solely on review texts also
demonstrated significantly higher accuracy across all productdomains in multiple metrics compared to simple extraction.
As an example of generated experiential description, table
VI shows translated sections for product description, experiential description, and justification, specifically for earphones.
The generated description certainly describes the noise cancellation performance of the earphones. The content is specificenough to understand the comfort of use.

TABLE V
AVERAGE RATINGS FOR THE BREAD PRODUCT CATEGORY ( COMPARED TO C O - OCCURRENCE ** p < 0.01, * p < 0.05)
Evaluation Item

Proposed Method

Review Learning

Co-occurrence

Without Justification

Naturalness
Experientialness
Correctness
Interestingness
Helpfulness
Evidentness

4.28**3.65**3.34**3.54**3.48**3.29**4.47
3.91**3.51**3.77**3.64**3.66

4.27
4.00
2.81
3.22
3.05
2.84

4.27
**3.45
2.66
*2.91
*2.82

TABLE VI
H IGH - SCORING E XAMPLE OF E ARPHONES IN THE P ROPOSED M ETHOD
Product Description

Experiential Description

Evidence

Delicate and rich sound (unique driver × high-res playback). Equippedwith Anker’s proprietary driver “A.C.A.A 3.0,” it realizes delicate andrich sound quality where even subtle sounds are audible, thanks to two
dynamic drivers. Supporting the high-quality codec LDAC, it transmits
three times more information than regular codecs (Bluetooth A2DP’s
SBC, 328kbps, 44.1kHz), allowing for faithful music reproduction tothe original sound. 360° audio experience with 3D audio. Anker’s
unique algorithm processes the sound source in real-time, providing
an acoustic experience like being at a live venue or cinema. The
gyroscope sensor detects head movements, constantly offering an
immersive musical experience. You can choose between Music Modeand Movie Mode. Ultra Noise Cancelling 2.0: Anker’s proprietary
Ultra Noise Cancelling 2.0 automatically adjusts the strength ofnoise canceling according to the surrounding noise level, maximizing
immersion in music without being affected by the environment. Healthmonitoring includes heart rate, stress checks, posture reminders, andworkout functions.
I use them regularly during my commute on the subway and airplanes.
Even in crowded trains, the noise canceling doesn’t cut off, and I haveno complaints.
Ultra Noise Cancelling 2.0: Anker’s proprietary Ultra Noise Cancelling
2.0 automatically adjusts the strength of noise canceling according to
the surrounding noise level, maximizing immersion in music without
being affected by the environment.

TABLE VII
H IGH - SCORING E XAMPLE OF C AMERAS IN THE P ROPOSED M ETHOD
Product Description

Experiential Description
Evidence

With technology developed for full-frame cameras, the accuracy,
speed, and tracking of eye detection have been greatly improved fromthis camera’s Eye AF. It’s now easy and reliable to continuously focus
on the eyes, even in moving portrait shots. Eye AF activates simply
by half-pressing the shutter, and you can switch between the left
and right eyes of the participant. Furthermore, ’Real-time Eye AF’is also compatible with certain animals. This allows for high-speed
and precise detection and tracking of the eyes of pets and wildlife. A
densely packed 425-point phase-detection AF sensor covers about 84% of the imaging area. Additionally, contrast AF points increased from
169 in this camera to 425. The conventional Lock-on AF has beenrevamped with the ’Real-time Tracking’ feature. You can designate
the subject, half-press the shutter, or touch it on the monitor, and thecamera will automatically track it with high accuracy. Silent shooting
is now available during high-speed continuous shooting. It allows
shooting in quiet scenes without worrying about shutter noise, ensuring
you don’t miss the decisive moment. It’s also compatible with AF-C,
Real-time Eye AF, and Real-time Tracking.
I photograph animals, so when I take close-up shots, the expressionsof the moving subjects are captured exactly as they are.
You can shoot quietly even in scenes where you want to be silent,
without worrying about shutter noise, ensuring you don’t miss thedecisive moment.

Other generated result is shown in the table VII as anadditional example regarding cameras. In this output example,
the specific information that the shutter is quiet was rewritten
as the experience of being able to approach an animal and takea picture without being noticed. Therefore, many participants
rated this review description as correct.
V. D ISCUSSION
In this section, based on the results obtained from theevaluation experiments, we discuss the effectiveness and characteristics of the proposed method. Overall, the experimental
results revealed a significant difference across all productProduct Description**3.82**3.84domains in multiple evaluation metrics, such as the level ofinterest in the product, compared to the baseline method. This
indicates that generative approaches can offer a more engaging
experiential description than simple extraction algorithms. We
will further examine the effectiveness of the proposed methodfrom several perspectives.
First, we consider the features that appear in each productdomain. For the earphones and camera domains, the proposed
method and the method that presents only the product description received equivalent scores in two metrics: interest in the
product and the depth of product understanding. Conversely,
for the T-shirt and bread domains, the method that presented
only the product description received higher scores for these
two metrics.
These products possess multiple specification items in thedomains of earphones and cameras. For instance, earphones
have various specifications like being wired or wireless, having
noise-canceling features, and the type of drivers used. A
product description stating “Equipped with noise-canceling
functionality” could correspond experientially as “Train noises
are not bothersome”, thus specifications can serve as thebasis for experiential descriptions. However, in the T-shirt andbread domains, the number of specifications written for the
product is limited. Subjective elements such as appearance
or taste are challenging to infer from product descriptions.
Therefore, products that do not have detailed specifications intheir descriptions may not be suitable for our method.
Considering the earphone and camera domains where ourmethod performed particularly well, we reflect on the effectiveness of presenting experiential descriptions. When comparing the proposed method to the one that only presents the product description, no significant difference was observed in both
metrics: interest in the product and depth of understanding.
Given that some of the experiential descriptions generated byour method clearly contradicted the original product descriptions, improving the accuracy of these narratives could further
demonstrate the effectiveness of presenting such descriptions.
The experiential descriptions generated in our method are
based on only one sentence from the product description,
discussing only one specification. However, the entire productdescription naturally encompasses all specifications of the
product. Thus, there’s an inherent difference in the amountof information between experiential descriptions and productdescriptions, which might have influenced the evaluations.
Next, we discuss the effectiveness of presenting the basis forthe product description. Comparing our method to the methodthat does not present this basis revealed significant differencesin all five metrics. This confirms that presenting the basis is
more effective than not doing so.
We also reflect on the characteristics of the algorithm used
to estimate the basis. Although the ablation-based estimation
received relatively high evaluations (3.39 out of five points),
many examples were generated where the basis was incorrect.
One reason could be redundant statements in the productdescriptions or multiple product features influencing a single
experience. Our method estimated the basis by masking one
sentence from the product description at a time. This approach
faced challenges when one specification presupposed the presence of another. Strategies like masking similar sections at theword level for training could be considered in the future.
Finally, we discuss using Different Fine-tuning Tasks for
GPT-2. A method that employed GPT-2, fine-tuned using only
review texts, achieved high ratings comparable to the proposed
method, despite being fed product descriptions not present
in the training data. This can be attributed to the numerous
instances where product descriptions and reviews contained
overlapping information. Reviews often included statements
such as “this specification makes it suitable for this application,” which would typically be found in product descriptions.
Vendors can draft product descriptions in the dataset from
Rakuten Ichiba used in this study. As a result, in addition
to catalog specifications and objective product details, somedescriptions integrated excerpts from reviews as “customer
voices” or included experiential statements as “salesperson
recommendations.” Such content possibly contributed to themodel’s high performance trained solely on reviews whengenerated from product descriptions.
VI. C ONCLUSION AND F UTURE C HALLENGES
This research proposed an algorithm that, when provided
with a product description, outputs an experiential statement
suited for it, and the corresponding basis within the productdescription. The proposed method utilized GPT-2, fine-tuned
with product descriptions and review texts, to generate theseexperiential descriptions. By creating review texts while obscuring parts of the product description, the study inferred
how specific contents in a product description would likely
influence certain review narratives.
A comparative evaluation of the outputs from the proposed method and other methods across six evaluation metrics was conducted through experimental evaluations. The
results reaffirmed the efficacy of generative approaches likethe proposed method and methods showcasing the underlying
basis. However, numerous instances where the presented basis
within the product description was incorrect were observed.
As detailed in section III-D, the current approach extracts the
top five terms based on simple occurrence probability andthen randomly selects and presents an experiential statement
containing any of these terms. Enhancing this process isanticipated to improve the quality of the presented experiential
descriptions and the accuracy of their corresponding basis.

Addressing these challenges is deemed as the future direction
for this research.
ACKNOWLEDGMENT
This work was supported by JSPS KAKENHI Grants Number 21H03775, 21H03774, and 22H03905. We used “Rakuten
Dataset 1 ” provided by Rakuten Group, Inc. via IDR Dataset
Service of National Institute of Informatics [10].
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1 Rakuten Dataset (in Japanese): https://rit.rakuten.com/data release/