Text Summarization for NLP: 5 Best APIs, AI Models, and AI Summarizers in 2025

How does text summarization work?

A litany of text summarization methods have been developed over the last several decades, so answering how text summarization works doesn't have a single answer. In fact, academic research shows the study of automatic text summarization began as early as 1958. This having been said, these methods can be classified according to their general approaches in addressing the challenge of text summarization.

Text summarization methods fall into two main categories: Extractive and Abstractive. Extractive methods extract the most pertinent information from a text, while abstractive methods generate novel text that accurately summarizes the original. Despite their relative complexity, abstractive methods produce much more flexible and arguably faithful summaries, especially in the age of Large Language Models.

Extractive text summarization methods

As mentioned above, Extractive Text Summarization methods work by identifying and extracting the salient information in a text. The variety of Extractive methods therefore constitutes different ways of determining what information is important (and therefore should be extracted).

For example frequency-based methods will tend to rank the sentences in a text in order of importance by how frequently different words are used. For each sentence, there exists a weighting term for each word in the vocabulary, where the weight is usually a function of the importance of the word itself and the frequency with which the word appears throughout the document as a whole. Using these weights, the importance of each sentence can then be determined and returned.

Graph-based methods cast textual documents in the language of mathematical graphs. In this schema, each sentence is represented as a node, where nodes are connected if the sentences are deemed to be similar. What constitutes "similar" is, again, a choice of different specific algorithms and approaches. For example, one implementation might use a threshold on the cosine similarity between TF-IDF vectors. In general, the sentences that are globally the "most similar" to all other sentences (i.e. those with the highest centrality) in the document are considered to have the most summarizing information, and are therefore extracted and put into the summary. A notable example of a graph-based method is TextRank, a version of Google's pagerank algorithm (which determines what results to display in Google Search) that has been adapted for summarization (instead ranking the most important sentences). Graph-based methods may benefit in the future from advances in Graph Neural Networks.

Abstractive text summarization methods

Abstractive methods seek to generate a novel summary that appropriately summarizes the information within a text. While there are linguistic approaches to Abstractive Text Summarization, AI models (casting summarization as a seq2seq problem) have proven extremely powerful on this front over the past several years, with research dating back to 2015 first applying deep learning to the task. The invention of the Transformer has therefore had a profound impact in the area of Abstractive Text Summarization, as it did to so many other areas.

More recently, Large Language Models in particular have been applied to the problem of text summarization. The observation of Emergent Abilities in LLMs has proven that LLMs are capable agents across a wide variety of tasks, summarization included. That is, while LLMs are not trained directly for the task of summarization, they become competent general Generative AI models as they scale, leading to the ability to perform summarization along with many other tasks.

More recently, LLM-based summarization-specific approaches have been explored, using pre-trained LLMs with Reinforcement Learning from Human Feedback (RLHF), the core technique which evolved GPT into ChatGPT (e.g. here and here). This schema follows the canonical RLHF training approach, in which human feedback is used to train a reward model, which is then used to update an RL policy via PPO. In short, RLHF leads to an improved, more easily prompted model that tailors its output to human expectations (in this case, human expectations for what a "good" summary is).

The field of text summarization is still an ongoing field of research, and there are natural extensions that can be explored in light of the work that has already been done. For example, we might consider using Reinforcement Learning from AI Feedback (RLAIF) instead of RLHF, as recent research demonstrates that RLAIF can achieve comparable performance to RLHF on tasks that include summarization.

Text summarization evaluation methods

Summary quality evaluation uses both automated metrics and human judgment.

Key evaluation metrics:

• ROUGE (Recall-Oriented Understudy for Gisting Evaluation): Measures word overlap between generated and reference summaries
• BLEU (Bilingual Evaluation Understudy): Compares n-gram matches with human-written references
• Human evaluation: Ultimate test for coherence and accuracy

These automated metrics provide benchmarking capability but don't capture semantic meaning perfectly. The most reliable evaluation combines automated scores with human review.

Choosing the right text summarization approach

Choosing the right summarization approach depends on your specific requirements. When evaluating vendors, research on AI integration shows that product teams prioritize cost (64%), quality and performance (58%), and accuracy (47%) above other factors.

• Extractive summarization works best for:
  • High factual accuracy needs (legal documents, compliance)
  • Quick identification of key passages
  • Cases requiring traceability to source text
• Abstractive summarization excels when:
  • Natural, readable summaries are priority
  • Processing conversational content (meetings, calls)
  • Generating novel content formats (headlines, social posts)

For many product teams, the flexibility of abstractive methods is worth the added complexity, especially with the power of modern AI models.

Best APIs for text summarization

Now that we've discussed what Text Summarization for NLP is and how it works, we'll compare some of the best Text Summarization APIs, AI summarizers, and AI Summarization models to utilize today. Note that some of these APIs support Text Summarization for pre-existing bodies of text, like a research paper, while others perform Text Summarization on top of audio or video stream transcriptions, like from a podcast or virtual meeting.

Quick comparison of top text summarization APIs:

1. AssemblyAI's summarization models

AssemblyAI is a Speech AI company building new AI systems that can understand and process human speech. The company's AI models for Summarization achieve state-of-the-art results on audio and video. In addition, AssemblyAI has additional Summarization models built for specific industry use cases, including informative, conversational, and catchy. Summaries can be returned as bullets, gist, paragraph, or headline (see example above).

For maximum flexibility, LeMUR (AssemblyAI's framework for Large Language Models) is the recommended way for product teams to generate summaries in any custom format by prompting an LLM with the transcript data.

In addition, AssemblyAI offers the Auto Chapters feature, which segments audio into chapters and provides a time-stamped one-paragraph summary and single-sentence headline for each.

AssemblyAI's AI models are used by top product teams in podcasts, telephony, virtual meeting platforms, conversational intelligence AI platforms, and more. The company's highly accurate transcription models, like Universal, are trained on millions of hours of audio data, which makes summaries generated from its transcriptions even more accurate and useful.

2. plnia's text summarization API

The plnia Text Summarization API generates summaries of static documents or other pre-existing bodies of text. In addition to Text Summarization, plnia also offers Sentiment Analysis, Keyword Extractor, Abusive Language Check, and more. Developers wishing to test plnia can sign up for a 10-day free trial; plans that include Text Summarization then start at $19 per month.

3. Microsoft Azure text summarization

As part of its Text Analytics suite, Azure's Text Summarization API offers extractive summarization for articles, papers, or documents. Requirements to get started include an Azure subscription and the Visual Studio IDE. Pricing to use the API is pay-as-you-go, though prices vary depending on usage and other desired features.

4. MeaningCloud's automatic summarization

MeaningCloud's Automatic Summarization API lets users summarize the meaning of any document by extracting the most relevant sentences and using these to build a synopsis. The API is multilingual, so users can use the API regardless of the language the text is in. Those looking to test the API must first sign up for a free developer account and then pricing to use the API ranges from $0-$999+/month, depending on usage.

5. NLP Cloud summarization API

NLP Cloud offers several text understanding and NLP APIs, including Text Summarization, in addition to supporting fine-tuning and deploying of community AI models to boost accuracy further. Developers can also build their own custom models and train and deploy them into production. Pricing ranges from $0-$499/month, depending on usage.

Text summarization tutorials

Want to try Text Summarization yourself? This video tutorial walks you through how to apply Text Summarization to podcasts.

Building products with summarization AI

Text Summarization is used across a wide range of industries and applications.

Use cases include:

• Creating chapters for YouTube videos or educational online courses via video editing platforms.
• Summarizing and sharing key parts of corporate meetings to reduce the need for mass attendance.
• Automatically identifying key parts of calls and flagging sections for follow-up via revenue intelligence platforms.
• Summarizing large analytical documents to ease readability and understanding.
• Segmenting podcasts and automatically providing a Table of Contents for listeners.

Getting started with text summarization

Text summarization transforms how we process information from documents, audio, and video files. It reveals insights that would otherwise stay buried in raw data.

Getting started doesn't require deep NLP expertise. Production-ready APIs handle the complexity, letting you focus on your application logic. You can try our API for free to experiment with summarization on your own files and start building immediately.

Additional Resources:

• How to use AI to automatically summarize meeting transcripts
• 3 easy ways to add AI Summarization to Conversation Intelligence tools
• How to use Speech to Text AI for Ad Targeting & Brand Protection
• How to use Speech AI systems for podcast hosting, editing, and monetization
• Build standout call coaching features with AI Summarization

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Frequently asked questions about text summarization

How does extractive text summarization differ from abstractive summarization in practice?

Extractive summarization selects and combines existing sentences from the original text, like highlighting key passages. Abstractive summarization generates entirely new text that paraphrases the main ideas in different words.

What are common misconceptions about text summarization accuracy?

A common misconception is that a "good" summary is always factually perfect. While extractive summaries are factually grounded in the source text, abstractive summaries can sometimes misinterpret context or generate plausible-sounding but incorrect information, an issue known as hallucination. In fact, one academic survey concluded that nearly 30% of summaries generated by abstractive systems did not match the facts in the source documents.

When should you use text summarization vs other NLP techniques?

Use text summarization when your goal is to reduce the length of a text while preserving its core meaning. Use other NLP techniques like sentiment analysis when you need to understand the emotion of a text, or named entity recognition when you need to extract specific pieces of information like names, dates, or locations.

How does text summarization relate to speech-to-text and audio processing?

Text summarization is a critical layer on top of speech-to-text. First, a speech-to-text model transcribes audio into a written transcript, then a text summarization model processes that transcript to create a concise summary.

What prerequisites are needed to implement text summarization effectively?

To use a text summarization API, you primarily need clean input text. If you're summarizing audio, this means using a highly accurate speech-to-text model first, as transcription errors will negatively impact summary quality.