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πŸ€– Joplin AI - Summarisation: The project aims to create note summaries to help users synthesize main ideas and arguments to identify salient points. This means that users will have a clear idea of what the note is about in a short piece of text with less mental effort.

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joplin/plugin-ai-summarisation

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πŸ€– Summarize your notes with Joplin AI!

1. Introduction

1.1 Motivation

The project aims to create note summaries to help users synthesize main ideas and arguments to identify salient points. This means that users will have a clear idea of what the note is about in a short piece of text with less mental effort.

Example Use Cases:

  • Assist in processing notes to improve efficiency: Distill critical information from notes, highlight key ideas and quickly skim notes.
  • Classify or cluster notes by their contents: Summarize key concepts from notes and use them in similar group notes. This could be used for tagging notes.
  • Distill important information from long notes to empower solutions such as search, question, and answer.

1.2 Types of Summaries

There are two main types of summarization: extractive and abstractive

● Extractive summarization: This method takes sentences directly from the original note, depending on their importance. The summary obtained contains exact sentences from the original text.

● Abstractive summarization: Abstractive summarization is closer to what a human usually does β€” i.e., conceive the text, compare it with their memory and related information, and then re-create its core in a brief text.

Abstractive summarization tends to be more computationally expensive since you must utilize neural networks and generative systems. On the other hand, extractive summarization does not require the use of deep learning and data labeling [1].

2. Usage

2.1 Hide/Show Panel

Starting the Joplin will at first make the Joplin AI Summarization panel appear. Users can hide/show panel by using keyboard shortcuts: command + shift + f (MacOS) and ctrl + shift + f (Windows).

2.2 Flowchart

flowchart LR
   A[Opening Joplin]-.-> B[Using the Panel]
   A[Opening Joplin]-.-> C[Using Context Menus]
   C -.-> D[Click on the Notebook]
   C -.-> E[Click on the Note]
   E -.-> F[Right-click on the note]
   E -.-> G[Highlight multiple text in the note]
   F -.-> H[Summarize the note]
   G -.-> I[Right-click on the text]
   I -.-> J[Summarize the highlighted text]
   B -.-> K[Click on the note in the notebook tree]
   K -.-> L[Edit the summary, configure length and choose different algorithms]
   L -.-> M[Click save]
   M -.-> N[edit, change font-weight, etc.]
   D -.-> O[Right-click on the notebook]
   O -.-> P[Summarize the notebook]
Loading

Panel

Clicking on the notes in the panel will also open notes in Joplin. There, you can craft your own summary by adjusting its length and generating multiple versions of summaries by performing various algorithms (LexRank, TextRank, LSA, KMeans Clustering) to find the best summary.

Joplin App

In Joplin, you can summarise notes by using:

  1. Note Context Menu
  2. Notebook Context Menu
  3. Editor Context Menu

3. Testing

Run unit tests by running npm run test. The testing framework that we are using is jest.

4. πŸ“Ή Video Demonstration

4.1 Panel

joplin-plugin-ai-summarisation-panel.mp4

4.2 Context Menus

joplin-plugin-ai-summarisation-context-menu.mp4

About the plugin

a. By Ton Hoang Nguyen (Bill) πŸ§‘β€πŸ’»: https://github.com/HahaBill

References

[1] IBM - Text Summarization https://www.ibm.com/topics/text-summarization

[2] Automatic Text Summarization Methods: https://arxiv.org/abs/2204.01849

About

πŸ€– Joplin AI - Summarisation: The project aims to create note summaries to help users synthesize main ideas and arguments to identify salient points. This means that users will have a clear idea of what the note is about in a short piece of text with less mental effort.

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