Text Summarization for NLP refers to the process of shortening papers, podcasts, documents, videos, and other large bodies of texts into their most important parts. This is done by leveraging Deep Learning and Machine learning models.
Typically, Text Summarization APIs summarize either pre-existing, or static, bodies of texts, like a legal document, or to audio streams, like a YouTube video, that is transcribed via ASR-technology like a top Speech-to-Text API.
Developers and enterprises alike use Text Summarization APIs to:
- Summarize large documents and flag sections for follow-up
- Create chapters for YouTube videos or videos shared via an educational course
- Automatically providing a Table of Contents for podcasts
- Summarizing calls in a cloud-based contact center to increase agent occupancy
And more.
This article compares APIs that perform Text Summarization on either category, or both, with top-rated accuracy.
Here are the 5 Best APIs for Performing Text Summarization for NLP:
1. AssemblyAI’s Auto Chapters API
AssemblyAI is a leading speech recognition provider with industry-best accuracy. In addition to its core transcription API, it offers a host of Audio Intelligence APIs that let users build high ROI features on top of their audio data. One of these Audio Intelligence APIs is Auto Chapters, its Text Summarization API. When users run an audio file through its Auto Chapters API, the API will return both a one paragraph summary and single headline for each “chapter,” or points where the audio naturally changes topics.
Pricing for its Auto Chapters API begins at $0.000583/second. Users can sign-up to test the API for free here.
2. NLP Cloud Summarization API
NLP Cloud’s Text Summarization API offers Summarization for pre-existing bodies of texts at decent accuracy, in addition to other NLP/NLU APIs. Its online community also offers AI models that its community of members can use to train, fine-tune, and deploy their own models into production. Users can access NLP Cloud’s APIs for free up to a certain usage each month, with plans then ranging up to $499/month.
3. Microsoft Azure Text Summarization
Microsoft Azure is another top name in the ASR and NLP world. Its Text Summarization API is offered as part of its Text Analytics suite and is available for static texts. Pricing for Azure varies significantly depending on usage and if other APIs are also needed, with a pay-as-you-go payment structure. You’ll also need an Azure subscription to get started.
4. plnia’s Text Summarization API
Plnia also offers a Text Summarization API for pre-existing bodies of text, in addition to other NLU APIs such as Abusive Language Check, Keyword Extractor, Sentiment Analysis, Article Extraction, Language Detection, and more. For those looking to test plnia, the company offers a 10-day free trial and then plans start at $19/month after that.
5. MeaningCloud’s Automatic Summarization
Finally, MeaningCloud offers an “Automatic Summarization” API for static bodies of text. Its model works by first extracting the most important sentences in the document and then using those sentences to build its summary. MeaningCloud’s API also supports a wide range of languages, so users can still use Text Summarization regardless of the text’s native language. Users looking to try MeaningCloud can sign-up for a free developer account; fees then range from $0-$999/month, depending on usage.
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