Shorten Paper

For convenience, apart of response time has been omitted from the demo. Shortening text is time-consuming due to the language model process. The actual response time will be longer than shown in the demo.

Demo_Video.mp4

Shorten Paper is a Python project that utilizes language models like GPTs to shorten highly lengthy documents while retaining important information.

Language models have a max token length in each call.

Therefore, Shorten Paper breaks down the given text into smaller chunks to handle lengthy texts with language model, preserving their coherence and flow, even after being split into smaller parts.

Also, you can adjust the shorten ratio of the given text length and the ratio of previous/next text chunk to be included in an api call.

Now, you can input the instruction to each file!

Requirements

• Docker-Compose - Install Guide
• OpenAI API Key - Configure API Key

Usage

To use Shorten Paper, follow these simple steps:

1. Clone this repository to your local machine.
2. Add your OpenAI API key to the OPENAI_API_KEY field in the .env file.
3. Modify the PAPERS_INPUT_DIR, PAPERS_OUTPUT_DIR, and OUTPUT_PREFIX fields in the .env file if necessary.
4. Place the target paper(s) in the PAPERS_INPUT_DIR.
5. Run 'run - mac, linux.sh' for Mac/Linux or 'run - windows.bat' for Windows to build and run the Docker container at the root project directory.

After building and running the Docker container, the program will automatically start running. It will read in all the text files in the PAPERS_INPUT_DIR, shorten them using the specified language model, and save the shortened versions to the PAPERS_OUTPUT_DIR.

The shortening settings can be modified in the .env file.

Shorten Paper currently supports text files with the following extensions: .txt, .csv, .pdf, .doc, .docx, .json, .xml, .yaml, .html, .md, .tex.

For optimal results, ensure that your document is well-organized and follows a logical structure. A well-ordered document will lead to a higher quality of shortening, allowing the program to preserve important information while reducing the overall length.

I recommend to repeat the shortening process (SHORTEN_REPEAT) rather than adjust extremely small shorten ratio (SHORTEN_RATIO) for the extremely long document. However, it will cost more accordingly.💸

Text Settings (.env)

The following are the available text settings that can be adjusted in the .env file:

SHORTEN_REPEAT (int): bigger than 0

Shorten provided document in SHORTEN_RATIO ratio of token length and repeat it SHORTEN_REPEAT times. In conclusion, the document will be shortened in (SHORTEN_RATIO ** SHORTEN_REPEAT) ratio of token length.
Usually, I use 3-5 for the academic papers.
The default value is 3.
❗ However, it will cost more accordingly.💸

SHORTEN_RATIO (float): (0, 1]

The shortening ratio of the original text.
The default value is 0.4.

PREVIOUS_TEXT_TOKEN_RATIO (float): [0, 1)

The ratio of the previous text to include when split the input text for the language model. 0 means to exclude the previous text in the shortening process.
The default value is 0.4.

NEXT_TEXT_TOKEN_RATIO (float): [0, 1)

The ratio of the next text to include when split the input text for the language model. 0 means to exclude the next text in the shortening process.
The default value is 0.2.

Additionally, you can consider to fine-tune the language model parameters for the better output quality:

• LANG_MODEL_NAME (str) : Models
• TEXT_TOKEN_LEN (int) : Max Token List
• MODEL_TEMPERATURE (float) : [0, 2]
• MODEL_TOP_P (float) : [0, 1]
• MODEL_PRESENCE_PENALTY (float) : [-2, 2]
• MODEL_FREQUENCY_PENALTY (float) : [-2, 2]

Result Comparison Sample

Target Paper

• VR-HandNet: A Visually and Physically Plausible Hand Manipulation System in Virtual Reality

Shortened Full Paper .pdf (SHORTEN_REPEAT=5, SHORTEN_RATIO=0.4)

This study introduces a new method for manipulating virtual objects using VR-HandNet. It maps the VR controller to the virtual hand and uses a deep neural network to determine desired joint orientations of the virtual hand model at each frame based on information about the virtual hand, VR controller input, and hand-object spatial relations. The proposed approach combines reinforcement learning-based training with imitation learning paradigm that increases visual plausibility by mimicking reference motion datasets. To evaluate this approach, 424 training datasets having 50,100 frames and 247 test datasets having 30,332 frames were collected using Oculus Quest HMDs/controllers under Unity engine implementation with Nvidia PhysX4.1 simulated physical skeletal models having capsule/box colliders detecting collision between hands/objects were used. The evaluation section classified test datasets into primitive-level objects (PL), scaled-primitive-level objects (SPL), and complex level objects (CL) further classified into reference pose (RP) and reference object (RO). This paper provides valuable insights into collecting reference motion data for virtual reality applications while introducing a promising approach to dexterous manipulation using physics-based approaches and deep reinforcement learning algorithms.

Paper's Original Abstract

This study aims to allow users to perform dexterous hand manipulation of objects in virtual environments with hand-held VR controllers. To this end, the VR controller is mapped to the virtual hand and the hand motions are dynamically synthesized when the virtual hand approaches an object. At each frame, given the information about the virtual hand, VR controller input, and hand-object spatial relations, the deep neural network determines the desired joint orientations of the virtual hand model in the next frame. The desired orientations are then converted into a set of torques acting on hand joints and applied to a physics simulation to determine the hand pose at the next frame. The deep neural network, named VR-HandNet, is trained with a reinforcement learning-based approach. Therefore, it can produce physically plausible hand motion since the trial-and-error training process can learn how the interaction between hand and object is performed under the environment that is simulated by a physics engine. Furthermore, we adopted an imitation learning paradigm to increase visual plausibility by mimicking the reference motion datasets. Through the ablation studies, we validated the proposed method is effectively constructed and successfully serves our design goal. A live demo is demonstrated in the supplementary video.

Credits

Shorten Paper was developed by Han DongHeun and using the language model provided by OpenAI, inspired by Auto-GPT

This project is my personal project for personal usage.

As you know, this kind of language agent highly relies on the language model's ability. So, I can't guarantee that it will always give you a high quality shortened result. Though, this project can be useful if you kindly fine-tune the parameters and clearly organize your document structure for your certain situation.

Hope this project will be helpful for your research or studies, or whatever purpose you may have.

Thank you for enjoying my work!