Generating Fine-Grained Aspect Names fromMovie Review Sentences Using
Generative Language Model
Tomohiro Ishii1 , Yoshiyuki Shoji1,2[0000−0002−7405−9270] ,Takehiro Yamamoto3[0000−0003−0601−3139] ,3[0000−0002−9492−2246]
Hiroaki Ohshima, Sumio Fujita4[0000−0002−1282−386X] , and
1[0000−0001−7568−0766]
Martin J. Dürst
1

Aoyama Gakuin University,
Sagamihara, Kanagawa 252–5258, Japan{ishii, duerst}@sw.it.aoyama.ac.jp
2
Shizuoka University,
Hamamatsu, Shizuoka 432–8011, Japanshojiy@inf.shizuoka.ac.jp
3
University of Hyogo,
Kobe, Hyogo 651–2197, Japant.yamamoto@sis.u-hyogo.ac.jp, ohshima@ai.u-hyogo.ac.jp
4Yahoo Japan Corporation,
Chiyoda, Tokyo 102–8282, Japansufujita@yahoo-corp.jp

Abstract. This paper proposes a method for identifying an aspect highlighted in a sentence from a movie review, utilizing a generative language model. For example, the aspect “SFX Techniques” is identified
for the sentence “The explosions in cosmic space were realistic.” Classically, aspects are commonly estimated in the field of opinion mining
within product reviews with classification or extraction approaches. However, because the aspects of movie reviews are diverse and innumerable,
they cannot be listed in advance. Thus, we propose a generation-based
approach using a generative language model to identify the aspect ofa review sentence. We adopt T5 (Text-to-Text Transfer Transformer),a modern generative language model, providing additional pre-training
and fine-tuning to reduce the training data. To verify the effectivenessof the learning techniques thus adopted, we conducted an experiment
incorporating reviews of Yahoo! movies. Manual labeling of the correctness and diversity of the aspect names generated shows that our method
can generates a variety of fine-grained aspect names using little trainingdata.
Keywords: Opinion Mining, Aspect Detection, Generative Language
Model

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T. Ishii et al.

1

Introduction

Even when they are watching the same movie, different people will pay attention
to different parts of it. Some will focus on the script and others on the actors’
performances. A person who has a specific interest may focus on a specific aspect.
For example, a railroad enthusiast may focus on whether the trains shown in amovie are correct in relation to the temporal and spatial setting of the movie.
Thanks to the rapid growth of information and communication technologies, the way that people watch movies and find reviews has changed in recent
years. Subscription-based distribution services have brought more encounterswith movies. People can decide what to watch from a vast pool of candidates.
Movie review services, such as IMDb, have become widespread. Reading review
posts to decide what movie to watch has become a part of everyday life.
However, the immense number of posted reviews makes it impossible to readthem all. Additionally, as a large and diverse group of users contribute reviews,
the aspects highlighted by different reviewers can vary. For instance, one user
may want to read reviews that discuss the accuracy of the historical details in amovie, but most reviews concentrate on the actors’ performances. Our hypothetical user will not be interested in this review. However, they cannot determine
whether a review suits their purpose without reading the reviews.
To address this issue, numerous studies have focused on estimating the aspects of the text of reviews. However, conventional aspect classification andextraction approaches require each description to be categorized into predetermined aspects or extracting aspect names from the sentences. For instance, in
common product domains, such as televisions or cameras, the sentence “I cansee fine details” would typically be linked to the aspect of “resolution.” At thesame time, “I can bring it anywhere” would be associated with aspects such as“weight” or “size”. For this categorization, a list of aspects must be provided inadvance.
Reviews of entertainment content, such as movies and books, are necessarily
more specific than reviews of traditional products. Different items may havedifferent aspects; for example, even within the set of movies, the names of the
aspects appearing in science fiction and romance movie reviews will be different.
The number of such aspects is innumerable, so they cannot be listed in advance.
New aspect names may become necessary when a new movie is released.
Therefore, this paper proposes a generative language model method that cangenerate aspect names from a given review sentence. Generative language models
are capable of generating abstract aspect names that traditional classificationand extraction approaches cannot address. For instance, take the case of creating
an aspect name for the sentence “The explosions in cosmic space were realistic.”
Here, the review text has not included the phrase “scientific accuracy,” and preenumerating specific aspect names for this case would be too fine-grained andlaborious. This paper leverages state-of-the-art generative language models tocomprehensively handle names of such fine-grained and specific aspects.
We adopt T5 (Text-to-Text Transfer Transformer), among the best-regarded
text-to-text large language models, for this purpose. An overview of our training

Generating Fine-Grained Aspect Names from Movie Review Sentences

3

T5 Training Phase
Additional pre-training
Masked Review

This movie is great. The violenceis typical Takeshi Kitano ﬁlm!

Traditional yakuza-like action iswonderful. I felt tired of the same
[MASK][MASK] in the last part ofthe movie.

Additional intermediate task
There should be no potatoes
in this time period.

Determine“Action” and “Scene”

lead actor.

outer space!

mischievous spirit of thedirector.

T5
True

It is strange to hear sound in

Text B： It's interesting to see thecontains

This sentence have aspect:

A sentence in review

Text A：I like the costume of theif a sentencePredict masked words

Fine-tuning

Two review sentences

an aspect

Masked term is...

Final Task

Fine-tuning

A sentence in review

Determine
if two sentences
contains thesame aspect

Generate aspect namefrom review sentence

Aspect of two sentences is...

not same

Aspect name is...

“scientiﬁc accuracy”

Actual Generation Phase
Arbitrary one sentence

It's weird how it's a 90's scene

but everyone has a smartphone.
Generate aspect namefrom review sentenceTrainedT5

Converting

trained model

Aspect name is･･･“historical accuracy”

Fig. 1. Overview of our training steps of T5. The model was trained with an additional
pre-training and two intermediate tasks before the main task.

method is shown in figure 1. First, using crowdsourcing, we created a dataset
consisting of sentences from reviews and the relevant aspect names. We thentrained T5 using the review sentences and aspect names. In general, training
a language model with an additional intermediate task improves the accuracyof its generation [10]. We interspersed two additional learning tasks that differ
from the generation of aspect names: a task to binary classify whether a review
contains a statement about an aspect and another to binary classify whether two
given sentences refer to the same aspect. This model with additional training
was fine-tuned with the task of generating aspect names.
The performance of this generative model was evaluated using real data.
Aspect names were generated for review sentences that were collected from real
online movie review sites. The experimental participants labeled each aspect
name, assessing its correctness and fine grain. Thus, we confirmed the presenceof fine-tuning and changes in accuracy depending on the additional tasks.

2

Related Work

This study was undertaken aims to identify the aspects mentioned in online
reviews. To provide context and positioning, this section describes related studies

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T. Ishii et al.

on language models in online reviews, aspect extraction, and summarization,using language models.
2.1

Language Model in Online Review

Traditionally, review data are used as a general source of information for research. In addition, applications used to retrieve reviews have been widely studied. In particular, in opinion mining, it is common to analyze sentiment fromproduct reviews.
For instance, Kim et al. [6] propose a sentiment analysis method using online
reviews. Singh et al. [14] also present a sentiment classification method for movie
reviews. Using SentiWordNet, they make classifications for emotional expressions
and their polarities with a focus on parts of speech and words in proximity. Xu
et al. [16] show a method that turns reviews into a source of knowledge that canbe used to answer users’ questions. Xu et al. [15] propose a simple CNN modelusing two types of pre-trained embeddings.
This research generally uses lexical, probability-based, and classical machinelearning approaches. In recent years, as large-scale language models (LLMs)
such as Transformers have received more attention, studies have come to focus
on them.
For instance, Rietzler et al. [13] propose a method using BERT (Bidirectional
Encoder Representations from Transformers) to classify the review aspect. Hasib
et al. also use BERT for classifying sentiments of reviews [2]. Karimi et al. [5]propose an architecture called BAT (BERT Adversarial Training). BAT applies
adversarial learning to post-training BERT. He et al. [3] propose a neural approach for finding coherent aspects. These studies use LLMs as classifiers. They
are therefore similar to the traditional approach to estimating aspects.
2.2

Aspect Extraction

Aspect extraction has long been a topic of interest in opinion mining. In review
posts, users can freely write their opinions about a product. To use reviewsin product search, visualization, and analysis, it is important for shoppers to
identify what aspects a specific description refers to [12].
Jo et al. [4] propose a method for automatically discovering different viewpoints and combinations of feelings on viewpoints from submission reviews. Peng
et al. [9] propose a framework for dealing with the aspect of sentiment triplet
extraction. Aspect sentiment triplet extraction refers to the task of extracting
triples that can indicate what an aspect is, its sentiment polarity, and why it
has this polarity. Angelidis et al. [1] present a neural network framework forsummarizing opinions drawing on online product reviews. In their network, they
combine two weak supervised components: an aspect extractor and a sentimentpredictor.
Studies related to aspects of reviews are commonly used in both the categorical approach, where the aspect is applied to pre-prepared candidates, and in

Generating Fine-Grained Aspect Names from Movie Review Sentences

5

extracting aspects that are written directly in the review. This study seeks toaddress this problem with a generative language model.
2.3

Text Summarization Using Language Model

This study uses a language model to generate names of aspect from review
sentences. This approach is similar to summarizing in that it extracts the subject
matter from a text, abstracts it, and expresses it in a few words. Summaries oftexts can be roughly divided into extractive and abstractive summarization;
however, our study relates to abstractive summarization. In recent years, it has
been common to use LLMs for abstractive summarization. Significant researchhas been conducted on summarization using a language model.
Among LLMs used in this area, Liu et al. [8] propose BERTSUM, an extractive summarization method using BERT. Pegasus, proposed by Zhang etal. [17], is another prominent example of a traditional language model based on
the summarization method. Pegasus is also an extension of BERT, characterized the use of Gap Sentence Generation for pre-training. Lewis et al. [7] present
Bidirectional and Auto Regressive Transformers, a denoising autoencoder for use
in pre-training sequence-to-sequence models.
Some methods based on BERT specialize in discrimination by embedding.
Generative language models such as GPT (Generative Pre-trained Transformer)
and T5 are good at abstractive tasks in particular. Our method adopted T5, agenerative LLM.

3

Aspect Name Generation with T5

This section describes a method for generating the names of aspects mentioned
in a given movie review text. Our method consists of three steps:– additional pre-training,– fine-tuning via an additional intermediate task, and– fine-tuning via the final task.
The additional pre-training is performed to ensure that the model understands
the particular vocabulary and knowledge related to movies. This additional intermediate fine-tuning is intended to focus the model on aspects in particular.
The final task is the task of actual generation task. It generates the names of
the aspects of any input review text. We collected actual review data and had
it labeled it using crowdsourcing to enable this training.
3.1

Creating Training Data Using Crowdsourcing

Initially, we created a dataset for use in machine learning. Actual movie reviews
were collected from real online movie review sites. These reviews were divided
into individual sentences through splitting at the periods. Crowd workers then6

T. Ishii et al.
Labeling Task for Movie Review Sentences
We are conducting a study to analyze what aspects of a ﬁlm each statement contained in a ﬁlm review focuses on.
To do this, we collect information on whether a given sentence in a review We collect information on what aspectsof the ﬁlm a particular sentence in the review refers to, such as "the music" or "the unexpectedness of thedevelopment". The following steps are used to determine what each sentence refers to. Please follow the steps
below to enter what aspects of the ﬁlm each statement refers to.

Please write a few words about the aspect that the bolded review text is referring to.
Please choose what opinion the bolded review text expresses.
If you cannot ﬁnd any aspect that the review text is referring to, please press "Change Review" button.

Example Images

Review Sentence

Review Sentence

As this is a theatrical version, it has more impact thantelevision. The ﬁght scenes are smooth and suﬃciently

violent. Although there are slower moments, the tempo
is good and it never becomes boring.

① What is the sentence in black text about? Please answer in a few words.

This sentence is about

Actionand the sentiment is

positive
negative② Is this sentence evaluating the aspect positively or negatively? Please choose.

Review Sentence

Overall, the drawings were good. The voice actors arealso well done, with a wide range of idol voice actorsthat are very popular these days. The portrayal of theFirst time I've seen an old movie in 4k quality and it's

beautiful. I love how sharp the action is! This is Bruce
Lee at his best!

heroine is a bit old-fashioned.

① What is the sentence in black text about? Please answer in a few words.

This sentence is about

Casting and the sentiment is

positive

This sentence is about

Actionand the sentiment is

positive

Review Sentence② Is this sentence evaluating the aspect positively or negatively? Please choose.

As this is a theatrical version, it has more impact thantelevision. The ﬁght scenes are smooth and suﬃciently
Review Sentence

Review Sentence

violent. Although there are slower moments, the tempo

Review Sentence

Is the new book out yet for that matter? There are otherI felt as if scales were falling from my eyes, wondering

All review statements herein are ﬁctitious review

Imagine how many years I have been waiting.

know the world of the past 10 years. I had no idea thatis in English and we used actual submitted reviews forthings that should be done before making a movie.

what Tokyo was like back then. It is true that I only
cars were so valuable!

statements. This is for privacy reasons, since the paper
the experiments. In reality, Japanese data were used.

Change Review

This sentence is about Original

and the sentiment isnegative

This sentence is about

and the sentiment is

positive

This sentence is about

Actionand the sentiment is

If the content is not related to the movie, please use thepositive
negativeis good and it never becomes boring.

① What is the sentence in black text about? Please answer in a few words.

"Change Review" button to go to the next review!

This sentence is about

The following 30 texts are the actual targets. Thank you!

Actionand the sentiment is

positive
negative② Is this sentence evaluating the aspect positively or negatively? Please choose.

Review 1:

The ﬁlm's second half is amazingly interesting compared to the boring scenes in the beginning.

In space scene, the feeling of being thrown out into outer space was so amazing that it gave me
goosebumps. One thing that bothered me was that a couple of actors were reading monotone
voice.

Change Review

This sentence is about

and the sentiment is

Review 2:

But then, Tarantino is a fanatic nowadays. He goes on and on about how good the Hollywood

ﬁlmmakers of yesteryear were. Inglorious Basterds and Once Upon a Time in Hollywood are bothstories about movies saving the world after all.

Fig. 3. Example image is presented tothe crowd workers. It includes a screenshot of the actual form, instructions in
red text, and an inputted example answer. (Translated from Japanese)

Change Review

Fig. 2. Screenshot of the system used
for labeling. Instructions are given at
the gray section at the top. Below are
six example images. Below these, the20 review sentences are listed.

identified the aspects that were mentioned in each sentence. In total, the aspectnames for twenty sentences were input by each of the 100 crowd workers.
When the crowd workers accessed the system, they were first presented withan instruction. This included the following background statement “This is anexperiment for analyzing reviews conducted at a university” and instructions
stating, “Please input names of aspects and sentiments (i.e., negative or positive)
for the 20 review sentences displayed below.” Additional detailed precautions arealso provided. Below the instructions, six example images of correct labeling arepresented. By clicking on an image, the workers could see examples of aspectnames that could be assigned to specific review sentences.
Then, workers read and labeled 20 sentences from actual reviews. Each sentence was displayed together with the surrounding text (i.e., the sentence before
it and the one after it). The sentence to be labeled was marked in black, andthe surrounding sentences were gray. Workers input the aspect names as free
keywords into text boxes. Simultaneously, they also input the sentiment related
to each aspect name. For example, a sentence like “The lead actress’s strange
pronunciation was rather refreshing” is a “positive” expression concerning “performance.” It should be noted that not every sentence displayed in black men-

Generating Fine-Grained Aspect Names from Movie Review Sentences7tioned any aspect. In this case, the workers could label a different sentence bypressing the button for “Change Review.” This skipped statement is recorded
and labeled as “no aspect.”
3.2

Additional Pre-training: Predicting Masked Term in Movie
Review Data

Additional pre-training is performed to prompt the language model to better
learn vocabulary on movies and general knowledge about them. Many proper
nouns and peculiar expressions appear in movie review data; This includes namesof directors, actors, series, etc.
The T5 model, which is publicly available, has been pre-trained on public
documents (e.g., Wikipedia, OSCAR, and CC-100). Because these documents
do not contain sufficient descriptions related to movies, we train the model withmovie review data. This model is pre-trained with masked language modeling,in the same way as was done by Raffel et al. [11] For additional pre-training, themodel should be trained on the same task as that when the original model was
created, only using different data.
3.3

1st Intermediate Fine-tuning: Binary Classification of Whether
a Sentence Includes an Aspect

Next, we attempt to improve the quality of the model by fine-tuning it by meansof intermediate tasks. In general, the accuracy of the final task can be improvedby passing it through a task that is different from the original objective. Specifically, when training data for the final task are not large enough for training, it
is adequate to fine tune it with different domains and tasks, so that it solves thesame task with data from other domains or solves different related tasks using
data from the same domain. Thus, here, the model solved tasks related to thegoal of making the model understand what an aspect is.
To help the model understand aspects, it solves the most straightforward
binary classification problem. In this task, the model receives one sentence andthen learns to return 1 if the received sentence mentions an aspect and 0 otherwise. For this training, the sentences that the crowdworkers skipped as having“no aspect,” and the sentences that were labeled with an aspect name were usedas the answer data.
T5 can be trained using a prefix, which makes the different tasks explicit.
Therefore, when fine-tuning this task, the prefix “contains-aspect:” was added.
3.4

2nd Intermediate Fine-tuning: Binary Classification of Whether
Two Reviews Have the Same Aspect

Next, we allow the model to solve a slightly more advanced intermediate task. It
receives two sentences and determines whether both mention the same aspect.
This task is intended to enable the model to learn the differences between aspects.

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T. Ishii et al.

The review sentences containing aspect names that were collected throughcrowdsourcing are used for the training. We randomly selected two arbitrary
sentences from the dataset and then combined the two sentences with a separator
token.
The input is a text with a prefix like “same aspect: typical Takeshi Kitano’s
violence scene [SEP] it has surprising end part.” In this example, the former
refers to the director and the latter to the story. Thus, the model was trained toreturn 0 because the two sentences mention different aspects.
We used this training because we wanted to tune the model more efficiently
using fewer training data. It is expensive to have people read sentences and label
them with aspect names is expensive. This method, however, can use many
combinations of two sentences in a given dataset, creating extensive training
data.

3.5

Final Fine-tuning: Aspect Name Generation

The model ultimately solved the same tasks as in the performance task. It input an arbitrary single sentence and outputs an aspect name. The prefix was
also used for this training. For instance, the model was trained to generateaspect names, such as “direction,” input such as the following is given: “aspectgeneration: I was impressed with the way he expressed the main character’s
feelings by making it rain.”
The same task was performed for the actual aspect name generation. A modeltrained in this way outputs aspect names even if they are input with review
sentences that are not included in the training data. In such a case, the common
linguistic sense obtained from training on public documents and knowledge ofthe movie studied in additional pre-training should be used. For example, thegenerated aspect name may be extracted from the review text, an abstractionof an expression in the review, or a completely new aspect name.

4

Evaluation

The evaluation tests were conducted to confirm the accuracy and effectivenessof the proposed aspect name generation and the effectiveness of the fine-tuning
through the intermediate tasks. The experiment was conducted in two parts:
first, a preliminary experiment was performed with automatic accuracy evaluation, followed by a main experiment that included subject evaluation. We
created a dataset through the collection of reviews from real review sites and labeled them using crowdsourcing. Multiple comparative models were created fortraining using different fine-tuning methods. For each of these models, we evaluated the reproducibility (automatic evaluation) of the generated aspect names,
their correctness, their fine granularity, and their novelty.

Generating Fine-Grained Aspect Names from Movie Review Sentences

4.1

9

DatasetWe collected reviews from Yahoo! Movies, among the most extensive review
sites in Japan. We extracted 176,970 of the reviews, avoiding those that were
extremely short or too minor (this number was constrained by the graphic memory on the video card that was used for the training). All of these review data
were first used for the additional pre-training of T5.
Next, sentences that were neither long nor short were extracted. We asked thecrowdworkers to label the review sentences with the name of the aspect that they
reviewed and to indicate whether they mentioned any aspect. Crowdsourcing
continued until 1,500 sentences were labeled with the aspect name and collected.
The labeled review sentences thus obtained were used to create data forintermediate tasks. In the first intermediate task (the binary classification ofwhether an aspect was included), 3,759 reviews were prepared. For the second,(binary classification of whether two sentences refer to the same aspect), 1,000
pairs of review sentences were prepared.
During the creation of a dataset for the second intermediate task, we addressed the distortion of the notation of the aspect name, e.g., “story” and“scenario,” were essentially the same aspect. For this purpose, the similarity of
two aspect names was calculated using Sentence-BERT, and aspect names witha semantic similarity of 0.9 or higher were considered identical.
In addition to the training dataset, a dataset for the subject experiments
was also generated. Review sentences not used for either training that have astandard length were extracted. We only used reviews that seemed to mention
an aspect that was used for evaluation. We classified such reviews using a simple
BERT classifier (accuracy 0.63).
4.2

Comparison Methods

We prepared different methods, and only some of the training was given to verify
the effectiveness of each of the trainings described in Section 3. Specifically,
one set was given no additional training at all, one set only had additional
pre-training, and one set had only a part of the intermediates task. For theintermediate tasks, we also compared which task was solved most rapidly.
Table 1 shows the 10 methods compared in the evaluation. The methods ofPre-training+Include>Same and Pre-training+Same>Include are theproposed methods (that is, these are the models that successfully completed all
training tasks).
4.3

Implementation

Hugging Face Transformers5 , a library of Transformer-based models was used to
implement T5. We used the Japanese T5 pre-trained model6 . Additional training
5
6

Hugging Face Transformer: https://huggingface.co/docs/transformers/index
sonoisa/t5-base-japanese: https://huggingface.co/sonoisa/t5-base-japanese10

T. Ishii et al.

Table 1. Comparison of methods for evaluation and their accuracy during preliminary
experiments. The precision assessment indicates whether the method generated thesame aspect name as the test data during cross-validation (using BERT for distortionof the notation). The bottom two are the proposed methods (the ones that successfullycompleted all training).
MethodPrecision in
F1 Score in
Additional Aarlier Later Final # aspect name # aspect names
generated
not in training data Auto Evaluation Auto Evaluation
Pre-training Task Task Task

FinalTask Only
None
FinalTask+Include
None
FinalTask+Same
None
FinalTask+Include>Same
None
FinalTask+Same>Include
None
Pre-training Only
Done
Pre-training+Include
Done
Pre-training+Same
Done
Pre-training+Include>Same Done
Pre-training+Same>Include Done

None None Done
Include None Done
Same None Done
Include Same Done
Same Include Done
None None Done
Include None Done
Same None Done
Include Same Done
Same Include Done

76
74
74
69
77
84
92
79
56
11

25
28
23
27
28
31
35
23
11
1

0.800
0.795
0.794
0.798
0.797
0.800
0.799
0.800
0.802
0.798

0.795
0.791
0.791
0.795
0.794
0.796
0.795
0.796
0.797
0.790

parameters were set as follows: Maximum sequence length, 512; batch size, 16;learning rate, 0.005; weight decay, 0.001; and warmup steps, 2,000.
The learning rate was 0.0003 (determined empirically). The other parameters
were set to the default values for Hugging Face Transformers. SentenceTransformers7 was used to evaluate whether the aspect name was correct. We used a
pre-trained multilingual model8 for it.
4.4

Preliminary Experiment: Automated Accuracy Evaluation

We first performed an automated cross-validation evaluation to roughly assess
the generation accuracy. In this evaluation, we split the dataset, consisting of
crowdsourced labeled review sentences and aspect names, into training and testsets.
The test set was then fed into the model trained on the training set. We
determined whether the generated aspect names matched the original manuallyassigned aspect names. However, to deal with the notation distortion, we used
BERT to determine the identity of the aspect name (as in subsection 4.1).
4.5

Experiment: Subject Evaluation of Generated Aspect Names

The participants manually assessed whether the aspect names generated for the
unknown sentences were correct. They read one review sentence and the aspectnames generated by each method and labeled them as correct or incorrect.
The three evaluation factors were as follows:– format: Whether the aspect name generated is a likely aspect name (0 or1),– correctness: How well the aspect name matches the content of the review
sentence (scale of 1 to 5), and7
8

SentenceTransformers: https://www.sbert.net/
Hugging Face sentence-transformers https://huggingface.co/sentence-transformers/

Generating Fine-Grained Aspect Names from Movie Review Sentences

11

Table 2. Ratings for each method in the subject experiment (normalized to 0 – 1).
MethodFormat Correctness Granularity

FinalTask Only
0.964
FinalTask+Include
0.969
FinalTask+Same
0.971
FinalTask+Include>Same
0.969
FinalTask+Same>Include
0.971
Pre-training Only
0.973
Pre-training+Include
0.962
Pre-training+Same
0.971
Pre-training+Include>Same 0.973
Pre-training+Same>Include 0.969

0.691
0.677
0.670
0.666
0.648
0.700
0.677
0.663
0.671
0.640

0.380
0.399
0.384
0.384
0.387
0.396
0.407
0.406
0.386
0.336

– granularity: Whether the aspect name is sufficiently fine-grained (scale of
1 to 5).
Three participants read 100 review sentences. A maximum of 10 aspect names(with duplicates removed) were appended to each review text in random order.
Participants labeled each aspect name.
4.6

ResultIn this section, the results of the preliminary experiment and the subject experiment are described. Table 1 presents the results for the preliminary experiments.
The model that had the highest accuracy had performed additional pre-training;
it determined whether the aspect was included first and determined the same
aspect name second. The models that had high accuracy had a lower probabilityof generating a new aspect name, often fitting an existing aspect names drawn
from the training data.
Table 2 presents the results of the subject experiment. The method of Pretraining Only was evaluated as having the highest score. However, the differences were minor, and the models’ performances were not significantly different
from each other. The granularity of the generated aspect names was determined
to be too rough in many cases.

5

Discussion

This section discusses the usefulness of the generative approach, the generation of
aspect names by T5, and the effectiveness of every additional training according
to the experimental results. Both the additional pre-training and the intermediate task showed increased accuracy. However, the additional pre-training wasextremely effective, although the intermediate task had a limited effect.
First, we discuss the results of the automatic evaluation. To clarify the accuracy, we focus on the F1 scores. As shown in Table 1, the model trained withall of the intermediate tasks achieved the highest accuracy. This model, developed with additional pre-training, first estimated the presence or absence of each

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T. Ishii et al.

Table 3. Example of a review text and the aspect name generated from it. The judgment is the evaluation of the appropriateness of the aspect name.
Review Sentences

Aspect Name

Type

Judge

I must miss it if it kept its original title.
Original Title
Extraction 1
It is from an era I am unfamiliar with, but I
Song
Extraction 1
recognized most of the songs.
I would have felt satisfied if they ended with a
Last Scene
Classification 1
in the final scene :)Regardless, it is undeniably a high-quality
Story
Classification 0
work that keeps you engaged till the end.
All the other actors fit their comic roles
Casting
Classification 1
perfectly and delivered high-quality performances.
I think that it is a black comedy that cleverly
Category
Classification 1
satirizes current social issues.
I think that it is a black comedy that cleverly
Story
Classification 0
satirizes current social issues.
It is good for amateurs, but the previous
Difference from Original Generation 1
anime version was better.
The strangeness of this movie must came
Director’s Personality Generation 1
out from a director’s taste.
The dancing has improved and the singing
Singing ability
Generation 1
is moving.
All the other actors fit their comic roles
Difference from Original Generation 1
perfectly and delivered high-quality performances.

MethodMany methods
Many methods
FinalTask Only
Many methods
Pre-training Only,
Pre-training+Include
Pre-training Only
Many methods
FinalTask+Include
Pre-training+Include
Pre-training+Include
FinalTask+Same,
Pre-training+Same

Table 4. The ten aspect names most frequently generated by each method (translated
from Japanese).
Pre-training
Only

Pre-training+Same

Pre-training+IncludePre-training+Same>Include

Pre-training+Include>Same

FinalTask
Only

FinalTask+Same

FinalTask+IncludeFinalTask
FinalTask+Same >Include +Include >Same

CinemaScale
NatureActing, Acting,... Dubbing
Tools
Translation
DramaEntertainment
CultureLove
ScaleFans’ Expectations Landscape
Love
Love
Expression
Tears
AttractionC
The Idea
Fatigue
Lighting
Spoilers
Love
Drama
Last Scene
Love
Passion
Love
BrainwashingSound Effects
Schedule
Screenplay Award Difference from Drama Description
Commentary
Evaluation Criteria
Doraemon’s tools Love
Spoilers
Kissing Scene
Compilation
Lacrimal GlandGeneration
Spoiler
Crying Scene
Cooking
Talk of Cast
Disappointment Dubbing
Language Difference
Scale
Last scene
Overseas travel Scenery
CookingSpecial Makeup Homage
Story
Cosmology
Brainwashing
Snow
Trailer
Disappointment
TargetOriginal Title
Brainwashing
Snow
Atmosphere of the Original Travel Expenses Dubbing
For Kids
Dubbing
Reproduction
TargetTravel Expenses
Overloaded
Original Title
Doraemon’s Tools Snow

Tools
Spoiler
Disappointment
Original
Inclusion
Dubbing
Travel Expenses
Animation
Feeling of Support
Meet Expectations

aspect and then determined whether the aspect of the two sentences was identical. This suggested that the accuracy of the model could be improved throughtraining it by solving aspect-related tasks.
In particular, fine-tuning with the intermediate task reduced accuracy insome cases. The model named Pre-Training+Same>Include, which trainedon all tasks, was the least accurate. However, fine-tuning in general tended tolead to higher accuracy. In all cases except Same>Include models, the pretrained models were more accurate than those models trained using the sametask. For instance, F1 value increased by 0.005 relative to FinalTask+Same
to Pre-training+Same. From these results, it appears that increasing theamount of data and focusing on additional pre-training may be a more efficient approach than performing fine-tuning through increasing the number ofintermediate tasks.
Next, we discuss the novelty, granularity, and quality of the aspects generated
and not their accuracy. Each model differed in terms of the number of unknown
aspect names that were generated. Some models did not generate novel aspect

Generating Fine-Grained Aspect Names from Movie Review Sentences13

names but forcibly classified review sentences with aspect names that were contained in the training data labels.
For example, the model Pre-training+Same>Include generated only one
new aspect name. It also tied all review sentences to a total of only 11 different
aspect names. The novelty and the diversity of the aspect names that weregenerated tend to weaken with the amount of training. Models that were trainedon both additional pre-training and two intermediate tasks output fewer aspectnames.
This may be due to an overfitting to the training data. It is possible thatthe model was over-trained to consider the definition of the aspect names shouldrefer only to the labels defined in the given dataset. There is room to investigate
this effect in the future by increasing or decreasing the data for training andchanging the ratio of fine-tuning.
Table 3 represents an example of the aspect names generated by the models9 .
Throughout this, reasonable aspect names are generated. These names included
those derived from extraction, classification, and generation. The aspect names
designated by extraction are those in which the model outputs words in thereview text as they are. That is, the model extracts words based on the inference
that these words are likely to be used as the aspect name. The aspect names
according to classification are those that are not directly included in the input
text but are included in the training data labels. Here, the model abstracts
the review text to choose a word; however, these aspect names simply entail aclassification as the pre-prepared aspect names. The aspect names according to
generation are the output terms that are not included in the review text or in the
training data labels. In these cases, the aspect names generated are completely
abstracted from the meaning of the review sentences, and the model infers themfrom nothing.
Here, our discussion focuses only on the aspect names generated. Table 4shows the top ten most frequent aspect names included in the output of themodels. Many models generate valid aspect names that are fine-grained andare not included in the data set, excluding Pre-train+Same>Include. The
only new aspect name generated by the Pre-train+Same>Include model was
an incorrect name, simply repeating the word “acting” several times. At othertimes, this model either extracted a term in the review or applied a sentence tothe label name in the training data.
One example of an incorrect aspect name was extraction using an incorrect
word tokenization. The model Pre-training+Include output the aspect name“C.” This indicates a failure to tokenize when reading the review text. The review
text included statements on visual expressions, such as, “This visual effect is thekind of expression I see in TVCM (Television Commercial Message).” Because
the social review includes data with many colloquial expressions, it may benecessary to proofread the text using conventional methods before performing
the language model.
9

For the sake of translation and anonymization, the reviews are fictitious, as theexperiment was in Japanese and uses real review sentences prepared by an individual.

14

T. Ishii et al.

In summary, T5 showed good performance, even without any fine-tuning or
additional training. We had thought that generating aspect names in a generative
language model would be more difficult. However, performing the same finetuning on plain T5 as in the production task achieved an F1 value of 0.79. In
addition, many of the aspect names generated were correct and were not found in
the training data. A larger dataset that includes more fine-grained aspect names
and that uses T5 in a straightforward manner could produce a more accurate
and practical language model.

6Conclusion

This paper proposed a method for generating aspect names for arbitrary sentences from reviews by training the generative language model T5. Evaluation
experiments showed that the generative approach can also generate aspect nameswith high accuracy. We found that fine-tuning using intermediate tasks was lesseffective, but additional pre-training was highly effective.
The contributions of this paper are as follows:– we revealed that a generative approach could be used for aspect name inferences, which is conventionally done using classification and extraction,and– we revealed that additional pre-training is effective for training generative
language models for the aspect name generation task.
In future work, we plan to enhance data cleansing and applied evaluation.
Specifically, we plan to construct a system for searching for review sentencesusing generated aspect names. Recently, methods involving more extensive andmore innovative language models have emerged (e.g., GPT-4 and Bard). It may
be possible to generate aspect names by prompts rather than using methods
based on fine-tuning. Significant room remains for improvement in aspect namegeneration by generative AI.

Acknowledgements
This work was supported by JSPS KAKENHI Grants Number 21H03775, 21H03774,
and 22H03905.

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