Summary of Topic-to-essay Generation with Knowledge-based Content Selection, by Jieyong Wang et al.
Topic-to-essay generation with knowledge-based content selection
by Jieyong Wang, Chunyao Song, Yihao Wu
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 novel copy mechanism model with a content selection module improves semantic coherence and generation diversity in the topic-to-essay generation task. By integrating rich semantic knowledge from language models into decoders, this approach outperforms state-of-the-art methods by 35% to 59%. The proposed method also adapts well to varying input complexities using an improved prefix tuning technique. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to generate text about a topic. It tries to make the generated text more diverse and coherent. To do this, it uses language models that have rich semantic knowledge. This approach works better than previous methods by generating text that is 35% to 59% more diverse while still being consistent with the topic. |
