Text Production with Deep Learning Approaches in Natural

Language Processing

M. Sharmila Devi, V. Lakshmi Chaitanya, V. Samatha, V. Naga Lavanya,

V. Srividya and M. Rehana

Department of Computer Science & Engineering, Santhiram Engineering College, Nandyal 518501, Andhra Pradesh, India

Keywords: Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Generative Pre‑Trained

Transformer (GPT), Bi Directional Encoder Representative for Transformer (BERT), Long‑Short Term

Memory (LSTM), Natural Language Processing (NLP).

Abstract: The majority of NLP applications revolve around text generation. It can be used, for instance, to generate

dialogue turns from dialogue moves, to express the content of knowledge bases, or to generate natural English

sentences from complex linguistic representations such as Abstract Meaning Representations or dependency

trees in data-driven systems. It uses Recurrent Neural Networks (RNNs), namely LSTMs and GRUs, for long-

range dependency capture and sequential data processing, enabling applications like text summarization and

machine translation. Examples of transformer models that demonstrate their many uses, from question

answering to code creation and creative writing, are GPT and BERT. Furthermore, additional relevant deep

learning techniques like hybrid models and convolutional neural networks (CNNs) are briefly discussed.

1 INTRODUCTION

The branch of artificial intelligence that gives

machines the ability to understand, read, and

deduce

meaning from human languages is known

as natural language processing, or NLP. The

translation of language structure and norms into

models that can understand breakdown and extract

significant facts from text is a combination of

computer science and linguistics as a topic of

study. NLP gives computers the ability to read and

understand human language using techniques like

machine learning for text analysis and

interpretation, allowing them to interact with

people in a natural way. NLP allows computers to

read, understand, and respond to human language,

which is more complex and subtler than

machine language and more natural to use when

interacting with technology

Lyu, Y., Adnan, A. B.

M., & Zhang, L. (2024)

. This helps to bridge the gap

between human and machine comprehension.

The figure 1 defines how NLP works to produce

the human understandable text which follows from

segmentation to the lemmatization by analyzing

each pattern of input.

Figure 1: NLP Process.

The components of NLP are

• Text Generation

• Text Summarization

• Information Retrieval

• Text Classification

Text Generation: The process of creating text that is

both grammatically correct and contextually

appropriate is known as text generation. The process

of text synthesis involves mimicking the linguistic

products of humans. Data preparation, model training,

Devi, M. S., Chaitanya, V. L., Samatha, V., Lavanya, V. N., Srividya, V. and Rehana, M.

Text Production with Deep Learning Approaches in Natural Language Processing.

DOI: 10.5220/0013891400004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 3, pages

55-62

ISBN: 978-989-758-777-1

text generation, and decoding strategies are all

included

Celikyilmaz, A., Clark, E., & Gao, J. (2020).

Text Summarization: By looking at the most

important words, sentences, and patterns, text

summarization condenses the text of lengthy

publications. The human effort required to read

lengthy papers is reduced by text summaries. There

are two kinds of text summarization.

• Summarization of extractive texts

• summarization of abstractive texts

Extractive Text Summarization: In order to create a

summary text that is equal to the input pattern and the

generated text or sentences that are included in the

input data, extractive text summarization involves

scanning through input data and analysing it to extract

important words, sentences, and phrases.

Abstractive Text Summarization: By reading and

analysing the input data in terms of important phrases

and paragraphs, abstractive text summarization

creates new text that may or may not be present in the

input data.

Information Retrieval: Information retrieval is the

process of recovering information from documents.

The repository manages the organization by storing,

retrieving, and analysing the information. It is crucial

to extract pertinent information from the massive

documents; we can retrieve the information by

searching for the information we need.

Text Classification: Text classification is the process

of dividing unstructured data into different categories

hear classifier is used to classify the input data

classifier focus on patterns of input data and divide the

input data based on different patterns text

classification is mainly used in business organizations

to define user data.

Deep Learning: Artificial neural networks are used

in deep learning, a subfield of machine learning, to

learn and predict from machines (

Mei et al., 2024).

Human-readable language is produced by deep

learning, which functions similarly to the human

brain. Three layers make to the deep learning process.

The input layer is where it first trains the input and

learns input patterns. The data being processed then

passes through a number of hidden layers that

transform the data into the desired output, which is

then accessible at the output layer. It uses a number

of strategies, including Generative Pre-trained

Transformers (GPT) like BERT (

Devlin, Jacob, et al.,

2019), GRU, and Recurrent Neural Networks (RNN)

using LSTM Staudemeyer, R. C., & Morris, E. R. (2019).

Convolutional Neural Networks (CNNs) are another

method. Figure 2 provides a clear explanation of how

deep learning, which consists of three layers, operates

from input to output.

Figure 2: Deep Learning Process.

By analyzing four different components of natural

language processing we are going to focus on text

generation and text summarization.

2 TEXT GENERATION

Text generation uses artificial intelligence,

specifically deep learning algorithms, to produce

human-readable text. It is capable of producing entire

texts as well as sentences and paragraphs. Text

generation is important because it makes it possible

to share knowledge, interact with others, and

communicate ideas, facts, and thoughts clearly

Celikyilmaz, A., Clark, E., & Gao, J. (2020). Text

production is important in a variety of domains,

including customer service, content creation, and

natural language processing. In order to convert input

data into output text, text generation uses algorithms.

Training models on vast volumes of text data in

order to learn grammar, context, and patterns. Using

circumstances or training data, this model applies

learned information to produce new text.

These models use deep learning techniques,

specifically neural networks, to understand sentence

structure and generate content that is both coherent

and appropriate for its context. Clear communication,

knowledge sharing, social interactions, and

information exchange are the purposes of text

generation. One of the challenges of text

generation is maintaining coherence. The generated

text has a consistent style. Managing Context to

generate relevant text. Diverse outputs will help you

avoid using the same words over and over again

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and provide a range of responses. Methods based on

deep learning perform well. We are using deep

learning algorithms since they are effective and we

need to overcome the obstacles.

2.1 Techniques

We use Recurrent Neural Networks (RNN) for text

generation, including Convolutional Neural

Networks (CNN), Long Short-Term Memory

(LSTM)

Staudemeyer, R. C., & Morris, E. R. (2019).,

Gated Recurrent Unit (GRU), Generative Pretrained

Transformer (GPT), and Bidirectional Encoder

Representations from Transformers (BERT). In order

to prevent grammatical errors in text production and

data management, we are using RNNs like LSTM in

this context. It will process and forecast the next word

or character in sequence mode, allowing the model to

learn lengthy documents and produce logical phrases.

Large datasets will be processed by this model, which

takes a word sequence as input and uses patterns it has

learnt to predict the next word. The model then uses

the training data to create new text.

It indicates that LSTMs

Staudemeyer, R. C., &

Morris, E. R. (2019). can effectively recognize the

connections between words in a phrase, which will

result in the creation of more grammatically sound

and coherent text. It also offers flexibility, as it can

generate various text kinds by training on various

datasets. It will analyse text sequences of varying

lengths, which is crucial for natural language

processing (NLP) activities when sentences have

varying word lengths. It will do this by mapping

complex linguistic patterns and the main advantage

over other approaches is that it will store data in word

sequences, making it possible to produce text, and it

will have more memory capacity to keep each node's

outputs for a longer period of time in order to

efficiently produce the following node's result. The

overall objective of BERT's bidirectional pretraining

methodology is to record contextual information from

left to right. It is mostly used to produce more logical

and contextually relevant content. It may also be used

to generate missing words in context and forecast

masked words within sentences. CNN will use filters

to extract features from text data in order to detect

complex features in the data. The main objective is to

identify patterns and extract features from a sentence

of text so that it can be used to create new text. It

learns to recognize word relationships and structures

to create text that is coherent and appropriate for the

context. This includes writing in different styles,

summarizing, creating creative content, and even

creating specific types of text, like poems.

• On the other hand, deep learning produces output

that is permanently improved as compared to

machine learning.

• Improved Efficiency and Scalability, which saves

time and effort when producing large amounts of

text.

• While it may not be feasible for humans to

produce greater creativity, artificial intelligence

will be able to produce new and innovative

material.

• Language accessibility will also offer translation

services, generating and translating text.

• It will also offer automatic feature extraction,

which means that it will automatically extract

features such as human names, location, and key

elements from any sentence or paragraph.

• Consistency and Accuracy, it means it will

maintains text style and grammar, minimizing

errors and ensures high quality output without

repetition.

2.2 Text Summarization

Text summarization is the process of summarizing the

large data and large documents in the short and

understandable form by taking the key information

and important words from the documents and produce

the summary is called text summarization. Text

summarization is a crucial in NLP by using the deep

learning techniques text summarization produce both

quality and efficient text .text summarization reduce

the human difficulty by producing the meaningful and

relevant text from the large data or from the large

documents. Text summarization categorized into two

components.

• Summarization of Extractive Texts

• Summarization of Abstractive Texts

Figure 3 provides the clear process about the

summarization of text from providing input to

produce the summarized output.

Text Production with Deep Learning Approaches in Natural Language Processing

Figure 3: Summarization of Text Process.

3 EXTRACTIVE

SUMMARIZATIONS

Extractive Text Summarization involves that it reads

from input data and analyses by finding important

words, sentences, phrases from the original text and

produce the summarized text same like input pattern

and produced text or sentences which are present in

input data. In extractive summarization it identifies

the key phrases, sentences or select the words based

on importance, frequency or relevance of text.

Abstractive summarization: Abstractive Text

Summarization involves that it reads and analyses the

input data based on important text and sentences or

paragraphs from original text and generate new

sentences or text with new words or sentences which

may or may not be in input data.it requires a more

understanding of text and generate coherent and

contextually meaning full text.

Deep learning techniques for text summarization:

We can summarize the text by using deep learning

techniques models to learn complex patterns and

generate quality summaries

Techniques: BERT, Attention Mechanism for

focusing on input, improve quality, relevance of

output, RNN, CNN.

GPT methodology Transformers' bidirectional

encoder representations (BERT) are used to generate

text learning from complex input. Sentence phrasing

using convolutional neural networks (CNN).

Recurrent neural network (RNN) with LSTM

Staudemeyer, R. C., & Morris, E. R. (2019). (Long Short-

Term Memory), GRU (Gated Recurrent Unit) for

sequential data and feature extraction. In the

sequence-to-sequence model, attention is used to

focus on input and produce pertinent information

from the input data mechanism. It uses both encoders

and decoders to analyses input data and produce

summaries. Input data is processed by encoder and

generates the summary by decoder. Sequence-to-

sequence models use recurrent neural networks

(RNN) with long short-term memory (LSTM)

networks.

We can produce high-quality summaries that are

logical, contextually meaningful, and produce

language that is human-like with the aid of deep

learning for text summarizing. The text can be

summarized by deep learning without the need for

manually created features and rules. Deep learning is

scalable across multiple languages and disciplines and

can handle vast amounts of data needing a large

amount of labelled data to produce understandable

text. Rare words might cause problems for deep

learning when it comes to text summarization,

making the summary inaccurate.

Extractive Text Summarization: Extractive text

summarization is a technique where it learns and

analyses from the input data and takes important

sentences and phrases to create a summary instead of

generating new sentences it selects important context

from original data.

Techniques: To extract the summary (

Zhong et al.,

2020)

from the given text we are going to use deep

learning techniques like RNN techniques like Long

Short-Term Memory (LSTM)

Staudemeyer, R. C., &

Morris, E. R. (2019).

and Gated recurrent unit (GRU)

for sequential data means it process the data based on

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continuity from training the next word patterns.

Figure 4 involves analyzing the input and

removing the nuisance data, unwanted data from the

given text called text processing. Then based on

retrieved data it extracts the features like repeated

words, sentence positioning from them it collects the

data called features extraction. From retrieved

sentences it involves in sentence scoring like sorting

the sentences based on importance and continuity

called sentence scoring.

Based on sentences scoring techniques select the

sentences importance called sentence selection and

then from all sentences it generates the summary by

using a methodology called BERT (

Devlin, Jacob, et

al.2019)

. Bert make sure to select the correct important

sentences from the given text to generate the

summary (

Zhong et al., 2020).

Figure 4: Process of Extractive Summarization.

Abstractive Text Summarization: Abstractive text

summarization is a technique which involves in

understanding the text from input data and generate

new sentences (

Lin, H. and Ng, V., 2019) instead of just

copying the sentences from the given content. It

processes the data just like humans and produce the

summary while keeping the main idea intact.

Techniques: Abstractive text summarization uses the

deep learning techniques like Generative pretrained

transformer (GPT) like Bidirectional encoder and

representative transformer (BERT) for sentence level

representation and learn the information from

complex data.

Figure 5: Process of Abstractive Summarization.

Figure 5 involves Process of abstractive

summarization includes understanding of in Input

text based on patterns and divide the text into tokens

and Generating the text into sentences which are

efficient and grammatically correct and paraphrasing

the text (

Lin, H. and Ng, V., 2019) which means

Reducing the summary by generating the new text by

learning and analysing the input text and abstract the

summary with respect to context.

4 PERFORMANCE METRICS

Perplexity: One of the most crucial measures in

natural language processing for evaluating a language

model's quality is perplexity, which also serves as a

reliable gauge of the model's coherence and fluency.

It gauges how accurately a probability model

forecasts a sample. The ability of a language model to

anticipate the following word or character based on

the context of the preceding words or Characters is

measured by perplexity. The reduced perplexity

indicates that the model is quite confident in its

predictions and does a good job of predicting the

sequence.

Probability distribution: When a language model

generates text, it assigns probabilities to each possible

word that could come next, based on the current

context.

4.1 Calculating Perplexity

• Log-likelihood: The model calculates the log-

probability of each word in the sequence based

on the words that came before it.

• Averaging: The log-probabilities average is

calculated.

• Exponentiation: The negative average log-

probability is multiplied to determine the

perplexity score.

𝑅𝑒𝑐𝑎𝑙𝑙 =

similar tokens

𝑇𝑜𝑡𝑎𝑙 𝑡𝑜𝑘𝑒𝑛 𝑖𝑛 𝑡𝑟𝑎𝑖𝑛𝑖𝑛𝑔

(1)

Precision =

 

  

 

(2)

Based on these probabilities, the model would

calculate a perplexity score. A lower perplexity would

indicate that the model is more confident in predicting

“mat” as the most likely next word.

Confusion matrix: As it provides a detailed

explanation of how well a model can classify

different classes and highlights areas where the model

Text Production with Deep Learning Approaches in Natural Language Processing

may be making mistakes between different types of

data, the confusion matrix is a popular performance

metric to evaluate the efficacy of deep learning

techniques, particularly for classification problems.

By comparing the values predicted by a model with

the actual values given a data set, a confusion matrix

helps assess how well categorization algorithms

perform in machine learning. The results of classifier

algorithms can be visually represented using a

confusion matrix, also known as an error matrix. A

confusion matrix may reveal that a model repeatedly

confuses cats and dogs in image recognition

applications, providing information on the model's

advantages and disadvantages rather than just its

overall accuracy.

Visual Representation: The matrix makes it easy to

understand how well the model performs for different

classes and displays the true positive (TP), true

negative (TN), false positive (FP), and false negative

(FN) classifications.

Detailed Analysis: While overall accuracy is a

common metric, a confusion matrix allows you to see

how well the model performs on specific classes,

which can be crucial when dealing with imbalanced

datasets.

Identify Misclassifications: By looking at the off-

diagonal elements of the matrix, you can pinpoint

which classes are most often confused with each

other, helping to guide model improvement

strategies.

4.2 Process of Confusion Matrix

• Generate the Matrix: After training your deep

learning model, use a function within your

machine learning library (like scikit-learn) to

calculate the confusion matrix on your test data.

• Interpret the Values: Analyse the diagonal

elements (correct predictions) and off- diagonal

elements (incorrect predictions) to understand

where the model is performing well and where it is

struggling.

• Calculate Derived Metrics: To further

understand the model's performance for each class,

calculate other important metrics including

precision, recall, and F1-score based on the

confusion matrix data.

5 RESULTS & DISCUSSIONS

To assess the potential of text generation and

summarization, there is a necessity for clear

evaluation metrices. For this we are using

performance metrics like perplexity for text

summarization, confusion matrix for text generation.

Figure 6: Confusion Matrix Score for Text Generation.

By using confusion matrix, it is observed that there is

efficient production of text by depicting the scoring of

sentences, patterns, phrasing, analysing the words,

grammatically correct context by al this confusion

matrix create a matrix in the form of rows and

columns and find the similarity by comparing the

rows and columns and remove the similarity, errors.

It compares from one row to one column and last row

to last column. By analysing figure 6 we can able to

determine the percentage.

Figure 7: Perplexity Score for Extractive Text

Summarization.

Perplexity performance metric used for the text

summarization where it detects the summary patterns

from first sentence to last sentence by analysing each

word, each letter, important sentences, new phrases.

It goes for the analysing whole input summary and

reduce the no of lines based on above mentioned

areas. From figure 7,8 it concludes the percentage of

extractive and abstractive text summarization.

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Figure 8: Perplexity Score for Abstractive Text

Summarization.

6 CONCLUSIONS

Deep learning provides a platform to work with large

and complex data. Dl techniques like LSTM, GRU in

RNN, and BERT in GPT for producing the efficient

text and for text summarization. DL provides wide

range of opportunities for researchers to learn and

evaluate more about text generation and

summarization. For better extraction of text

summarization uses the dl techniques like CNN,

RNN. The goal is not only to produce the human

understandable text but also efficient and

grammatically correct text. For text summarization

goal is to minimize the summary based on context and

importance. For evaluating the performance uses the

perplexity, confusion matrix. Perplexity check for

how efficient the sentences are generating based on

grammar, confusion matrix check for how efficiently

the sentences are generating based on patterns. The

common differences encountered in generation is

sometimes it gives less accurate text with errors in

grammar. For summarization it gives repetition of

sentences, lack of sentence accuracy.

7 FUTURE SCOPE

7.1 Automatic Text Summarization of

Superior Quality

Scope: Improving AI models to efficiently condense

massive text datasets while retaining crucial

information.

Solution: To guarantee greater accuracy and

relevance, use hybrid deep learning models (CNN +

RNN + Attention Mechanism) for abstractive and

extractive summarization.

7.2 Enhanced Information Retrieval and

Classification

Scope: Creating more efficient AI systems to retrieve

and classify information from vast data stores.

Solution: Leverage deep learning methods like

CNNs for feature extraction and NLP methods such

as named entity recognition (NER) in order to

enhance search precision and classification accuracy.

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