Summary of Research on Information Extraction Of Lcsts Dataset Based on An Improved Bertsum-lstm Model, by Yiming Chen et al.
Research on Information Extraction of LCSTS Dataset Based on an Improved BERTSum-LSTM Model
by Yiming Chen, Haobin Chen, Simin Liu, Yunyun Liu, Fanhao Zhou, Bing Wei
First submitted to arxiv on: 26 Jun 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 paper proposes an improved method for extracting critical information from Chinese news articles using an enhanced BERTSum-LSTM model. The goal is to create concise and clear news summaries that focus on main content while avoiding redundancy. The researchers tackle challenges such as complex semantics, massive information volume, and linguistic peculiarities like polysemy and word segmentation. By improving the BERTSum-LSTM model, they achieve better performance in generating Chinese news summaries. Experimental results demonstrate the effectiveness of their proposed method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to summarize Chinese news articles using artificial intelligence. The goal is to make news summaries easier to understand and shorter while keeping the important information. They use an improved computer model to help with this task, which handles challenges like complex meanings in Chinese text and large amounts of information. By improving their computer model, they get better results for summarizing news. |
Keywords
» Artificial intelligence » Lstm » Semantics
