Summarize by Segment

POST {{baseUrl}}/summarize-by-segment

Request Body

{"source"=>"We’ve all experienced reading long, tedious, and boring pieces of text - financial reports, legal documents, or terms and conditions (though, who actually reads those terms and conditions to be honest?).  Imagine a company that employs hundreds of thousands of employees. In today's information overload age, nearly 30% of the workday is spent dealing with documents. There's no surprise here, given that some of these documents are long and convoluted on purpose (did you know that reading through all your privacy policies would take almost a quarter of a year?). Aside from inefficiency, workers may simply refrain from reading some documents (for example, Only 16% of Employees Read Their Employment Contracts Entirely Before Signing!).   This is where AI-driven summarization tools can be helpful: instead of reading entire documents, which is tedious and time-consuming, users can (ideally) quickly extract relevant information from a text. With large language models, the development of those tools is easier than ever, and you can offer your users a summary that is specifically tailored to their preferences.  Let's take legal documents, for example. Though they are written in English, many people find legal documents to be difficult to comprehend, as if they were actually written in a foreign language. Moreover, the interesting parts of each document may differ depending on the person who reads it, so off-the-shelf summarization tools may be too general or too specific. As an example, let's look at the involved personas:", "sourceType"=>"TEXT", "focus"=>"AI-driven summarization"}

RESPONSES

status: OK

{"id":"dd5abb6a-d9af-9d8e-875b-fe18cf928b11","segments":[{"summary":"AI-driven summarization tools can help employees extract relevant information from long, tedious, and boring pieces of text, instead of reading entire documents, which is tedious and time-consuming.","segmentText":"We’ve all experienced reading long, tedious, and boring pieces of text - financial reports, legal documents, or terms and conditions (though, who actually reads those terms and conditions to be honest?).\n\nImagine a company that employs hundreds of thousands of employees.\n\nIn today's information overload age, nearly 30% of the workday is spent dealing with documents.\n\nThere's no surprise here, given that some of these documents are long and convoluted on purpose (did you know that reading through all your privacy policies would take almost a quarter of a year?).\n\nAside from inefficiency, workers may simply refrain from reading some documents (for example, Only 16% of Employees Read Their Employment Contracts Entirely Before Signing!).\n\nThis is where AI-driven summarization tools can be helpful: instead of reading entire documents, which is tedious and time-consuming, users can (ideally) quickly extract relevant information from a text.\n\nWith large language models, the development of those tools is easier than ever, and you can offer your users a summary that is specifically tailored to their preferences.","segmentHtml":null,"segmentType":"normal_text","hasSummary":true,"highlights":[{"text":"tedious, and boring pieces of text","startIndex":36,"endIndex":70},{"text":"This is where AI-driven summarization tools can be helpful: instead of reading entire documents, which is tedious and time-consuming","startIndex":745,"endIndex":877},{"text":"quickly extract relevant information from a text.","startIndex":899,"endIndex":948}]}]}