Spacy for Named Entity Recognition and LLMs for Text Summarization and Headline Generation.
Hey🙋♂️let’s look at how we can apply NER, Text Summarization and Headline Generation to attempt to solve some business use cases. I have always thought of these 3 concepts as ways organizations can improve user experience directly (Users interacting with a service that is powered by these 3 concepts) or indirectly (Operations Team using services powered by the 3 concepts to improve customer experience and service).
Play with the demo here!🔛https://ner-summary-headline.onrender.com/
Think through the following scenario of a customer support agent and how they can leverage these tools …🤔
A customer support agent at an e-commerce company could use text summarization to generate a summary of a customer ticket that describes a problem with an order. The agent could then use NER to identify the key entities in the summary, such as the customer’s name, order number, and the product they are having problems with. This information would allow the agent to quickly understand the issue and provide the customer with a resolution.
If you are still not yet sure how you can exploit these tools read through my second proposal…🤔🤔
Text summarization can be used to summarize legal documents such as contracts and court rulings, making it easier for lawyers and other legal professionals to quickly understand the key points of each document. NER can be used to identify key people, places, and organizations mentioned in legal documents, which can be used to categorize documents and improve search functionality. Headline generation can be used to automatically generate titles for legal documents based on their content. Document searchability is made easier.
Text summarization is a natural language processing (NLP) technique that condenses long passages of text into shorter, more concise versions while preserving the key information. It is a valuable tool for a variety of business use cases, including:
- Customer support. Text summarization can be used to quickly generate summaries of customer tickets or support requests, making it easier for customer support agents to understand the issue and provide a resolution.
- Content marketing. It can be used to create shorter versions of blog posts, articles, and other content for social media or email marketing campaigns. This can help businesses reach a wider audience and increase engagement.
- Market research. It can be used to summarize customer reviews, social media posts, and other market research data. This can help businesses identify trends, understand customer sentiment, and make better product and marketing decisions.
Advantages of text summarization:
- Saves time and improves efficiency.
- Improves comprehension of complex texts.
- Identifies key information and insights.
- Reduces the risk of missing important information.
Named entity recognition (NER) is another NLP technique that identifies and classifies named entities in text, such as people, places, organizations, events, and products. NER is used in a variety of business applications, including:
- Regulatory Compliance. NER can also be used to ensure regulatory compliance. For example, it can identify whether certain required information (like specific legal terms or clauses) is present or missing in a contract.
- Understanding Customer Preferences. By identifying and analyzing the entities mentioned in a customer’s interactions with your business, you can gain insights into their preferences and behaviours. This can help you tailor your marketing and sales strategies to different customer segments.
- Pattern Recognition. By identifying and linking entities, patterns can begin to emerge. These patterns can then be used to predict future fraudulent activities. For instance, if a particular pattern of transactions is often associated with fraud, future transactions that follow the same pattern can be flagged for further investigation. Here, we can use ASR engine to generate the text and NER to analyze the text and lastly, model a relationship that identifies whether the call in suspicious or not.
- Improved customer service. NER can be used to identify customer pain points and to develop solutions to these pain points. This can lead to improved customer satisfaction and loyalty. For example, a company could use NER to identify the most common customer complaints about its products or services. This information could then be used to improve the company’s products or services and to develop new marketing campaigns that address these customer concerns. An example of a company that uses NER for market research is Netflix. Netflix uses NER to extract insights from customer reviews and social media posts. This information is then used to improve Netflix’s recommendations engine and to develop new content that is likely to be of interest to its customers. As a result, Netflix can provide a better customer experience and potentially increase its customer retention rate.
Advantages of named entity recognition:
- Data Structuring: NER helps convert unstructured text into structured data by identifying and categorizing key entities such as person names, organizations, locations, and more. This structured data is easier to analyze and can lead to more accurate insights.
- Identifies key entities in text quickly and efficiently.
- Provides valuable insights into data.
A headline generator is a tool that uses AI to generate click-worthy headlines for articles, blog posts, and other content. Headline generators can be used to improve the performance of content marketing campaigns by increasing click-through rates (CTRs).
Advantages of headline generators:
- Save time and effort.
- Generate click-worthy headlines that increase CTRs.
- Improve the performance of content marketing campaigns.
- Identify the best headlines for different audiences and topics.
- Test different headlines to see which ones perform best.
Training the NER model.
What is Spacy?
SpaCy is a free, open-source library for advanced Natural Language Processing (NLP) in Python1. It’s designed specifically for production use and helps you build applications that process and “understand” large volumes of text1. It can be used to build information extraction or natural language understanding systems, or to pre-process text for deep learning.
The details of the initialized parameters of the trained NER model:
A summary of the training config file…
This configuration would be used to create an NLP model in SpaCy that can convert text into vectors and identify named entities in English text. The model would be trained on batches of 100 examples at a time. An embedding layer in a SpaCy NLP model that uses multiple hash functions to embed various attributes of tokens into vectors of a specified width is initialized.
The model will be trained on the train_corpus and then validated on the dev_corpus to check its performance. An interesting parameter to tune is patience = 100 (This sets the patience for early stopping. If the model’s performance doesn’t improve for this many steps, training will be stopped early).
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For Text summarization and Headline Generation I used facebook/bart-large-cnn and t5-small-headline-generator Huggingface inference endpoints on the same input data to the Ner Model for predictions.
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You can tune your model to fit your business objective. This Demo shows that indeed you can train a NER model to identify entities and use text summarization, headline generation and world cloud to quickly help make sense of unstructured data.
In conclusion, text summarization, NER, and headline generation are powerful technologies that have a wide range of applications in various industries. By leveraging these technologies, businesses can improve their efficiency, accuracy, and overall quality of service.
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