Record Once, Post Everywhere

Automatic Shortening of Audio Stories for Social Media

Bryan Wang , Zeyu Jin, and Gautham J. Mysore

Univeristy of Toronto and Adobe Research
In Proceedings of the ACM Symposium on User Interface Software and Technology (UIST ’22).

Paper Audio Data

What is ROPE?

When posting audio or video to social media, has it been a pain to edit it down to the platform's length limits each time? When it involves spoken-word (like talking head videos), you need to first figure out what parts of the story to remove and then laboriously and precisely edit it out. Who has time for that!?

Well, we have a solution for you.

ROPE is an intelligent system that algorithmically shortens recorded content to any specified length. You focus on creating content, and ROPE does the rest. It uses NLP to figure out which parts to remove with minimal effect to the storyline and then auto-edits it out. In this paper we investigate shortening audio stories but our algorithm tends to generalize well to video as well.

Formative Studies

We conducted formative studies to characterize the content people would record for short-form audio stories and to understand the limitation of audio time-stretching, a commonly used shortening technique for audio/video content on social media platforms. The findings motivated the design of our system’s shortening pipeline.
Study 1: What are short-form audio stories?

Despite the abundance of edited short-form audio content on the internet, original unedited recordings are typically unavailable. To kickstart research in automatic editing and retargeting of short-form audio content, we collected a dataset of unedited audio story recordings on Amazon Mechanical Turk.


We observed a common topic-based structure of the audio stories we collected, with sentences with different functionalities colored differently. An audio story usually consists of the main topic, multiple relevant sub-topics, and an ending:

  • Main Topic: The exposition, or the hook. The story's first sentence typically establishes the theme/context by reiterating or answering the prompts (teal).
  • Sub-topics: Following the exposition, there may be several sub-topics that are relevant to the main theme. In addition to the topic sentence (blue), supporting sentences (yellow) were sometimes used to provide further information.
  • Ending: To conclude the audio story, the speakers usually echo the exposition to provide a high-level summary of the whole story (teal). Some may end the story in more creative ways, such as making a joke or posing an intriguing question.

Study 2. Speed-up vs. Naturalness

Audio time stretching alters an audio signal’s playback speed and duration, and is commonly used for video and audio editing when ed- itors want to conform longer material to a designated time slot. Though widely used on short-form platforms like TikTok and Youtube Shorts, audio time stretching usually introduce artifacts. The artifacts could go unnoticeable for a minimal speed-up but become more prominent as the degree of stretching increases–the words become less intelligible as phonemes are clus- tered together. Though intuitively understandable, the relationship between speeding up and the naturalness/intelligibility of speech is unclear, let alone in the context of social media audio stories. Therefore, we conducted a listening study to investigate how much speed-up, when exceeded, would result in perceivable degradation of naturalness and intelligibility.


In our study with Amazon Mechanical Turk, we observed a steady monotonic decreasing naturalness rating as the speed-up factor increased. There is a slight but noticeable dip at 110% speed up, indicating that the turkers start to perceive a reduction of naturalness compared to unedited samples. There is a slight but statistically significant dip at 120% speed-up (p-value < 0.001, compared with 125%) that may be considered a soft limit for speed-up without significantly impacting the naturalness of the recording.

Algorithm Pipeline


The ROPE pipeline consists of 1) audio preprocessing, 2) sentence score calculation, and 3) combinatorial optimization.

We formulate automatic shortening as a combinatorial optimization problem to select optimal sentence combinations complying with length constraints. Our algorithm first transcribes the recording and segments it into sentences. It then selects optimal sentence subsets from the original recording by maximizing the total sentence score given the length constraints. We designed the sentence score function to consider both a sentence’s duration and its relevance to the summary of the audio story, obtained with neural abstractive summarization, in the sentence embedding space. We use dynamic programming for efficient optimization, which runs in real-time. Once the optimal selection is obtained, ROPE synthesizes the final audio output by cropping and concatenating the selected sentences. We also apply an audio enhancement technology to increase sound quality


For the full walkthrough of the ROPE system and example results, please watch the demo video below.

While the original algorithm is designed for audio, we also apply it to shortend social videos and found impressive results. In the below, the left is the 60s original tiktok video and the right is a 30s shortended version created by ROPE's algorithm.

Video copyrights belong to Sugoimart.

Data Release

We are working with the legal to release our collected audio story dataset. Please stay tuned!

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