Email Thread Summary Dataset

Email Thread Summary Dataset

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Email Thread Summary Dataset

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Email Thread Summary Dataset

Use Case

Email Thread Summary

Description

Explore our comprehensive Email Thread Summary Dataset with over 4,000 threads and 21,000+ emails. Perfect for NLP research, text summarization models.

Description:

The Email Thread Summary Dataset offers a rich collection of email threads with detailed human-generated summaries. This dataset is essential for research in Natural Language Processing (NLP) and text summarization, providing a structured foundation for various analytical purposes.

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Email Thread Details:

The dataset includes comprehensive details for each email thread, such as subject, timestamp, sender, recipient lists, and the body content of emails. The columns are well-defined to enhance usability for various data manipulation tasks:

  • thread_id: Unique identifier for each thread.
  • subject: The subject line of the email thread.
  • timestamp: Time when the message was sent.
  • from: The email sender.
  • to: List of recipients (available in CSV and Pickle formats).
  • body: The full content of the email.

This structure allows the extraction of relationships, time-based patterns, and the in-depth study of conversation flow.

Email Thread Summaries:

Human annotators have provided concise summaries for each thread, offering a high-level view of email content. This feature is highly valuable for training text summarization models and email communication research.

  • thread_id: Corresponding thread identifier.
  • summary: Brief human-generated summaries of the email thread.

Dataset Statistics:

  • Total Threads: 4,167
  • Total Emails: 21,684

Languages:

  • English (en)

Use Cases:

  1. NLP Research: Analyze email contents and summaries to improve NLP models, particularly in contextual understanding and conversation flow.
  2. Text Summarization: Use the dataset to train and evaluate AI models designed for summarizing large email threads.
  3. Email Analytics: Understand communication dynamics, such as sender-receiver interactions, content trends, and linguistic patterns within email threads.

File Formats:

  • CSV: Compatible with most data analysis tools.
  • Pickle (pkl): Efficient for Python-based data workflows, specifically for handling lists of recipients.
  • JSON: Provides flexibility for modern web-based applications and analytics platforms.

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