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Summary of The Branch Not Taken: Predicting Branching in Online Conversations, by Shai Meital et al.


The Branch Not Taken: Predicting Branching in Online Conversations

by Shai Meital, Lior Rokach, Roman Vainshtein, Nir Grinberg

First submitted to arxiv on: 21 Apr 2024

Categories

  • Main: Computation and Language (cs.CL)
  • Secondary: Machine Learning (cs.LG)

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
The paper presents a novel approach to predicting branching in multi-participant discussions, which can aid in summarization and thread disentanglement. The authors define the task of branch prediction and propose GLOBS (Global Branching Score), a deep neural network model that leverages structural, temporal, and linguistic features to predict branching. GLOBS is evaluated on three large Reddit discussion forums, achieving significant improvements over competitive baselines and demonstrating better transferability. The study finds that structural features contribute most to GLOBS’ success, and that branching tends to occur in earlier levels of the conversation tree.
Low GrooveSquid.com (original content) Low Difficulty Summary
The paper predicts what happens next in online conversations by creating a new topic or “branch”. It helps computers understand when people decide to stop talking about something and start something new. The authors create a special model called GLOBS to do this, and test it on lots of real online discussions. They find that their model works well and is better than other ways they tried. This can help us make online spaces where people are more likely to have meaningful conversations.

Keywords

» Artificial intelligence  » Neural network  » Summarization  » Transferability