{"id":3676,"date":"2025-02-14T17:08:42","date_gmt":"2025-02-14T17:08:42","guid":{"rendered":"https:\/\/www.siteuptime.com\/blog\/?p=3676"},"modified":"2025-02-25T08:29:35","modified_gmt":"2025-02-25T08:29:35","slug":"how-to-develop-an-ai-text-summarization-model-detailed-guide-for-developers","status":"publish","type":"post","link":"https:\/\/www.siteuptime.com\/blog\/2025\/02\/14\/how-to-develop-an-ai-text-summarization-model-detailed-guide-for-developers\/","title":{"rendered":"How to Develop an AI Text Summarization Model &#8211; Detailed Guide for Developers"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">Most of you may have heard of or even used AI-text summarizing apps or tools, right? They condense lengthy text pieces such as blogs, research papers, and essays, into concise summaries while keeping the original quality and meaning unchanged.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">These apps are widely used by students, teachers, writers, researchers, and even marketers globally. So, being a developer have you ever wondered how such apps can be created? If you have, then this detailed guide is especially for you.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In this blog, we are going to explain a step-by-step procedure through which developers can create a\u00a0<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Skills Required for the Text Summarizer Development<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Before we go towards the steps, we should first understand what sort of skills you need to possess to complete the model development.<\/span><\/p>\n<p><b>NLP knowledge:<\/b><span style=\"font-weight: 400;\"> A branch of artificial intelligence that enables machines or tools to effectively comprehend both the language and context of input text.<\/span><\/p>\n<p><b>Python:<\/b><span style=\"font-weight: 400;\"> It is high-level programming that is widely used for the creation of websites, online tools, and platforms.<\/span><\/p>\n<p><b>Deep Learning Frameworks:<\/b><span style=\"font-weight: 400;\"> You must have a strong understanding of specific deep learning frameworks such as <\/span><a href=\"https:\/\/www.tensorflow.org\/\"><span style=\"font-weight: 400;\">TensorFlow<\/span><\/a><span style=\"font-weight: 400;\">, <\/span><a href=\"https:\/\/pytorch.org\/\"><span style=\"font-weight: 400;\">Pytorch<\/span><\/a><span style=\"font-weight: 400;\">, and more.<\/span><\/p>\n<p><b>Datasets:<\/b><span style=\"font-weight: 400;\"> These contain several metrics and information for quick loading and preprocessing of datasets.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Step-by-Step Procedure for Using an AI Text Summarizing Tool<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Below are the steps that you need to follow to develop a text summarizing model that is backed by AI technologies.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">1.Decide the Working Approach of the Summarization Model:<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">First of all, you need to decide about the working approach of the summarization model. There are two options to choose from:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Abstractive approach:<\/b><span style=\"font-weight: 400;\"> In this type, your created model will generate a summary by using new words and phrases that do not appear in the source text.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Extractive approach<\/b><span style=\"font-weight: 400;\">: This has the opposite working; it works by creating a summary by using the same wording as the input text.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">It is important to note that both these require a different development approach. In this guide, we will be building a summarization model working on an abstractive approach.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">2.Download and Import Libraries:<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">From here, the process of development will take off. You need to download and import essential libraries discussed below.<\/span><\/p>\n<p><b>Transformers:<\/b><span style=\"font-weight: 400;\"> These provide numerous pre-trained models i.e., BART for effectively fine-tuning the text summarization model.<\/span><\/p>\n<p><b>NLTK:<\/b> <a href=\"https:\/\/www.nltk.org\/\"><span style=\"font-weight: 400;\">Natural language toolkit<\/span><\/a><span style=\"font-weight: 400;\"> that facilitates different tasks such as tokenization, stop removal, and more tasks.<\/span><\/p>\n<p><b>FastAPI:<\/b><span style=\"font-weight: 400;\"> It is a framework that will be used for building REST APIs for effectively deploying the summarization model.\u00a0<\/span><\/p>\n<p><b>ROUGE Score:<\/b><span style=\"font-weight: 400;\"> This one is used to determine the quality of summaries generated by the app.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">You are required to download, install, and import all these libraries. For installation, this command will be used:\u00a0<\/span><\/p>\n<table>\n<tbody>\n<tr>\n<td><span style=\"font-weight: 400;\">pip install transformers datasets nltk fastapi<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\">Whereas, for importing, the \u201c<\/span><b><i>Import<\/i><\/b><span style=\"font-weight: 400;\">\u201d command will be used.\u00a0<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">3.Data Collection\u00a0<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Remember, the foundation of any AI-powered summarization model lies in data collection. The more datasets it will be trained on, the more perfect results it can generate.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">So, devote significant time and effort to collecting a bunch of data from internet resources such as blogs, journals, scientific papers, social media posts, and more. There are also pre-made datasets available online. One of the most popular ones is the <\/span><b>CNN\/Daily Mail dataset<\/b><span style=\"font-weight: 400;\">.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">We have downloaded this dataset in a CSV file for each handling and importing. You are expected to do the same as well.\u00a0\u00a0<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">4.Import &amp; Preprocess the Data:<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Now, it is time to import all the collected data. The code that will be used is below:\u00a0<\/span><\/p>\n<table>\n<tbody>\n<tr>\n<td><span style=\"font-weight: 400;\">from datasets import load_dataset<\/span><\/p>\n<p><span style=\"font-weight: 400;\">dataset = load_dataset(&#8220;cnn_dailymail&#8221;)\u00a0<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\">When you are done with importing, then comes data preprocessing. This process involves different tasks such as tokenization where the text is divided into small chunks of words and sub-words.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The process also includes \u201c<\/span><b><i>Stop word<\/i><\/b><span style=\"font-weight: 400;\">\u201d removal in which commonly used words and phrases are removed from the collected data without damaging the original meaning. Below, we have mentioned the Python code that can be used for seamless data preprocessing.\u00a0<\/span><\/p>\n<table>\n<tbody>\n<tr>\n<td><span style=\"font-weight: 400;\">import nltk<\/span><\/p>\n<p><span style=\"font-weight: 400;\">from nltk.tokenize import word_tokenize<\/span><\/p>\n<p><span style=\"font-weight: 400;\">from nltk.corpus import stopwords<\/span><\/p>\n<p><span style=\"font-weight: 400;\"># Download necessary NLTK data<\/span><\/p>\n<p><span style=\"font-weight: 400;\">nltk.download(&#8216;punkt&#8217;)<\/span><\/p>\n<p><span style=\"font-weight: 400;\">nltk.download(&#8216;stopwords&#8217;)<\/span><\/p>\n<p><span style=\"font-weight: 400;\"># Sample text<\/span><\/p>\n<p><span style=\"font-weight: 400;\">text = &#8220;Input your text here.&#8221;<\/span><\/p>\n<p><span style=\"font-weight: 400;\"># Tokenization<\/span><\/p>\n<p><span style=\"font-weight: 400;\">tokens = word_tokenize(text)<\/span><\/p>\n<p><span style=\"font-weight: 400;\"># Remove stopwords<\/span><\/p>\n<p><span style=\"font-weight: 400;\">stop_words = set(stopwords.words(&#8220;english&#8221;))<\/span><\/p>\n<p><span style=\"font-weight: 400;\">filtered_tokens = [word for word in tokens if word.lower() not in stop_words]<\/span><\/p>\n<p><span style=\"font-weight: 400;\"># Lemmatization<\/span><\/p>\n<p><span style=\"font-weight: 400;\">lemmatizer = WordNetLemmatizer()<\/span><\/p>\n<p><span style=\"font-weight: 400;\">lemmatized_tokens = [lemmatizer.lemmatize(word) for word in filtered_tokens]<\/span><\/p>\n<p><span style=\"font-weight: 400;\">print(&#8220;Processed Tokens:&#8221;, lemmatized_tokens)<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\">Preprocessing the collected data will ensure you now have a text that is perfect for model training.\u00a0\u00a0<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">5.Model Architecture:<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The model plays a crucial role in making your text summarization model work effectively. There are several models available; a good and popular option is transformers. Their specific models such as <\/span><b>BERT<\/b><span style=\"font-weight: 400;\"> and <\/span><b>BART<\/b><span style=\"font-weight: 400;\"> are too good at handling long-range dependencies.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">These can be easily pre-trained on massive corpora and fine-tuned for numerous text summarization tasks. We are going to use the BART model; its implementation is easy. You just need to import \u201c<\/span><b><i>Pipeline<\/i><\/b><span style=\"font-weight: 400;\">\u201d which contains all the pre-trained text summarization from transformers.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Below we have mentioned its simplified code that you can use.\u00a0<\/span><\/p>\n<table>\n<tbody>\n<tr>\n<td><span style=\"font-weight: 400;\">from transformers import <\/span><span style=\"font-weight: 400;\">pipeline<\/span><\/p>\n<p><span style=\"font-weight: 400;\"># Load summarization pipeline<\/span><\/p>\n<p><span style=\"font-weight: 400;\">summarizer = pipeline(&#8220;summarization&#8221;, model=&#8221;facebook\/bart-large-cnn&#8221;)<\/span><\/p>\n<p><span style=\"font-weight: 400;\"># Input text<\/span><\/p>\n<p><span style=\"font-weight: 400;\">text = &#8220;&#8221;&#8221;Input your text here.&#8221;&#8221;&#8221;<\/span><\/p>\n<p><span style=\"font-weight: 400;\"># Generate summary<\/span><\/p>\n<p><span style=\"font-weight: 400;\">summary = summarizer(text, max_length=50, min_length=25, do_sample=False)<\/span><\/p>\n<p><span style=\"font-weight: 400;\">print(&#8220;Summary:&#8221;, summary[0][&#8216;summary_text&#8217;])<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\">By using this code, you can ensure a seamless implementation of the text summarization model.\u00a0<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">6.Model Training:<\/span><\/h3>\n<p><b>Feed this in mind:<\/b><span style=\"font-weight: 400;\"> the more effectively the model will be trained, the better output summaries it will create. The process involves configuring the model and then optimizing its parameters accordingly.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Many functions need to be performed in this regard. For instance, the loss function guides the text summarization model to learn from its own mistakes and come up with better results next time.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Here, we are again going to use the BART model. We need to import all of its relevant functions which are: <\/span><b>BartForConditionalGeneration, BartTokenizer, Trainer, TrainingArguments<\/b><span style=\"font-weight: 400;\">.<\/span><\/p>\n<table>\n<tbody>\n<tr>\n<td><span style=\"font-weight: 400;\">from transformers import BartForConditionalGeneration, BartTokenizer, Trainer, TrainingArguments<\/span><\/p>\n<p><span style=\"font-weight: 400;\">from datasets import load_dataset<\/span><\/p>\n<p><span style=\"font-weight: 400;\"># Load dataset<\/span><\/p>\n<p><span style=\"font-weight: 400;\">dataset = load_dataset(&#8220;cnn_dailymail&#8221;, &#8220;3.0.0&#8221;)<\/span><\/p>\n<p><span style=\"font-weight: 400;\"># Load pre-trained model and tokenizer<\/span><\/p>\n<p><span style=\"font-weight: 400;\">model = BartForConditionalGeneration.from_pretrained(&#8220;facebook\/bart-large-cnn&#8221;)<\/span><\/p>\n<p><span style=\"font-weight: 400;\">tokenizer = BartTokenizer.from_pretrained(&#8220;facebook\/bart-large-cnn&#8221;)<\/span><\/p>\n<p><span style=\"font-weight: 400;\"># Tokenize data<\/span><\/p>\n<p><span style=\"font-weight: 400;\">def preprocess_function(examples):<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0\u00a0\u00a0\u00a0inputs = [doc for doc in examples[&#8220;article&#8221;]]<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0\u00a0\u00a0\u00a0model_inputs = tokenizer(inputs, max_length=1024, truncation=True)<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0\u00a0\u00a0\u00a0with tokenizer.as_target_tokenizer():<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0labels = tokenizer(examples[&#8220;highlights&#8221;], max_length=128, truncation=True)<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0\u00a0\u00a0\u00a0model_inputs[&#8220;labels&#8221;] = labels[&#8220;input_ids&#8221;]<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0\u00a0\u00a0\u00a0return model_inputs<\/span><\/p>\n<p><span style=\"font-weight: 400;\">tokenized_datasets = dataset.map(preprocess_function, batched=True)<\/span><\/p>\n<p><span style=\"font-weight: 400;\"># Training arguments<\/span><\/p>\n<p><span style=\"font-weight: 400;\">training_args = TrainingArguments(<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0\u00a0\u00a0\u00a0output_dir=&#8221;.\/results&#8221;,<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0\u00a0\u00a0\u00a0evaluation_strategy=&#8221;epoch&#8221;,<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0\u00a0\u00a0\u00a0learning_rate=2e-5,<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0\u00a0\u00a0\u00a0per_device_train_batch_size=4,<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0\u00a0\u00a0\u00a0num_train_epochs=3,<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0\u00a0\u00a0\u00a0weight_decay=0.01,<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0\u00a0\u00a0\u00a0save_total_limit=2,<\/span><\/p>\n<p><span style=\"font-weight: 400;\">)<\/span><\/p>\n<p><span style=\"font-weight: 400;\"># Trainer<\/span><\/p>\n<p><span style=\"font-weight: 400;\">trainer = Trainer(<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0\u00a0\u00a0\u00a0model=model,<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0\u00a0\u00a0\u00a0args=training_args,<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0\u00a0\u00a0\u00a0train_dataset=tokenized_datasets[&#8220;train&#8221;],<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0\u00a0\u00a0\u00a0eval_dataset=tokenized_datasets[&#8220;validation&#8221;],<\/span><\/p>\n<p><span style=\"font-weight: 400;\">)<\/span><\/p>\n<p><span style=\"font-weight: 400;\">trainer.train()<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<h3><span style=\"font-weight: 400;\">7.Implementation:<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Now that your text summarization model is trained \u2013 it is now time to implement it. Here are the deep learning frameworks TensorFlow and PyTorch that we installed in the second step above.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Both these frameworks ensure a seamless implementation. Here again, we will use BART, and the code is mentioned below.\u00a0<\/span><\/p>\n<table>\n<tbody>\n<tr>\n<td><span style=\"font-weight: 400;\">from transformers import BartTokenizer, BartForConditionalGeneration<\/span><\/p>\n<p><span style=\"font-weight: 400;\"># Load pre-trained BART model and tokenizer<\/span><\/p>\n<p><span style=\"font-weight: 400;\">tokenizer = BartTokenizer.from_pretrained(&#8220;facebook\/bart-large-cnn&#8221;)<\/span><\/p>\n<p><span style=\"font-weight: 400;\">model = BartForConditionalGeneration.from_pretrained(&#8220;facebook\/bart-large-cnn&#8221;)<\/span><\/p>\n<p><span style=\"font-weight: 400;\"># Input text<\/span><\/p>\n<p><span style=\"font-weight: 400;\">text = &#8220;&#8221;&#8221;Input your text here.&#8221;&#8221;&#8221;<\/span><\/p>\n<p><span style=\"font-weight: 400;\"># Tokenize input<\/span><\/p>\n<p><span style=\"font-weight: 400;\">inputs = tokenizer.encode(&#8220;summarize: &#8221; + text, return_tensors=&#8221;pt&#8221;, max_length=512, truncation=True)<\/span><\/p>\n<p><span style=\"font-weight: 400;\"># Generate summary<\/span><\/p>\n<p><span style=\"font-weight: 400;\">summary_ids = model.generate(inputs, max_length=50, min_length=25, length_penalty=2.0, num_beams=4, early_stopping=True)<\/span><\/p>\n<p><span style=\"font-weight: 400;\">summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)<\/span><\/p>\n<p><span style=\"font-weight: 400;\">print(&#8220;Summary:&#8221;, summary)<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\">So, now, all you need to do is combine this code with the above ones and see your developed text summarization working.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">However, this isn\u2019t the last step, there are two more that you need to perform.\u00a0<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">8.Evaluation:<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The name is already telling everything \u2013 in this step, you need to evaluate the summarization model to determine its output quality.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For this, the ROUGE metric (a load metric in datasets) can be used to measure the overlap between the output summary and the reference summary to evaluate quality.\u00a0<\/span><\/p>\n<table>\n<tbody>\n<tr>\n<td><span style=\"font-weight: 400;\">from datasets import load_metric<\/span><\/p>\n<p><span style=\"font-weight: 400;\"># Load metric<\/span><\/p>\n<p><span style=\"font-weight: 400;\">rouge = load_metric(&#8220;rouge&#8221;)<\/span><\/p>\n<p><span style=\"font-weight: 400;\"># Example summaries<\/span><\/p>\n<p><span style=\"font-weight: 400;\">generated_summary = &#8220;input text here.&#8221;<\/span><\/p>\n<p><span style=\"font-weight: 400;\">reference_summary = &#8220;input text here.&#8221;<\/span><\/p>\n<p><span style=\"font-weight: 400;\"># Compute ROUGE scores<\/span><\/p>\n<p><span style=\"font-weight: 400;\">scores = rouge.compute(predictions=[generated_summary], references=[reference_summary])<\/span><\/p>\n<p><span style=\"font-weight: 400;\">print(&#8220;ROUGE Scores:&#8221;, scores)<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\">By performing this step, you will get insights about how good summaries your developed summarization model is generating.\u00a0<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">9.Deployment:<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Finally, the most loved part of development has arrived. This step refers to making your summarization model accessible to common users.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">You can either transform it into an application or a web-based tool. The FastAPI can be used for seamless integration into both web and mobile applications. The integration will allow users to quickly summarize lengthy text on the go.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">So, after going through all these ways, you will have an efficient idea of how to build a text summarization application.\u00a0<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Example of AI Text Summarization App<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">To demonstrate how exactly your developed model will work, we have found a decent AI-powered <\/span><a href=\"https:\/\/www.summarizer.org\/\"><span style=\"font-weight: 400;\">text summarization<\/span><\/a><span style=\"font-weight: 400;\"> app. It performs abstractive summarization and uses NLP and machine learning technologies for maximum quickness and accuracy.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">A screenshot of its input and output is attached below; check it out.<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-3687\" src=\"https:\/\/www.siteuptime.com\/blog\/wp-content\/uploads\/2025\/02\/download-300x144.png\" alt=\"\" width=\"650\" height=\"312\" srcset=\"https:\/\/www.siteuptime.com\/blog\/wp-content\/uploads\/2025\/02\/download-300x144.png 300w, https:\/\/www.siteuptime.com\/blog\/wp-content\/uploads\/2025\/02\/download-1024x491.png 1024w, https:\/\/www.siteuptime.com\/blog\/wp-content\/uploads\/2025\/02\/download-768x368.png 768w, https:\/\/www.siteuptime.com\/blog\/wp-content\/uploads\/2025\/02\/download.png 1248w\" sizes=\"(max-width: 650px) 100vw, 650px\" \/><\/p>\n<p>&nbsp;<\/p>\n<h2><span style=\"font-weight: 400;\">Final Words<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Text summarizing apps and tools have become quite popular in recent years. As a developer, you might want or be asked to develop such solutions. In such a situation, don\u2019t panic, as this isn\u2019t a hectic task if you follow the right approach. In this detailed guide, we have explained a step-by-step procedure for developing an AI-backed text summarizing model.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Most of you may have heard of or even used AI-text summarizing apps or tools, right? They condense lengthy text pieces such as blogs, research papers, and essays, into concise summaries while keeping the original quality and meaning unchanged. These apps are widely used by students, teachers, writers, researchers, and even marketers globally. 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