Summary of Evaluating Deduplication Techniques For Economic Research Paper Titles with a Focus on Semantic Similarity Using Nlp and Llms, by Doohee You et al.
Evaluating Deduplication Techniques for Economic Research Paper Titles with a Focus on Semantic Similarity using NLP and LLMs
by Doohee You, Samuel Fraiberger
First submitted to arxiv on: 2 Oct 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 presents efficient deduplication techniques for a large dataset of economic research paper titles in the field of natural language processing (NLP). It explores various pairing methods and established distance measures like Levenshtein distance and cosine similarity, as well as a semantic evaluation model called sBERT. The study finds that duplicates may be relatively rare based on semantic similarity across different methods. To confirm this, the authors use a human-annotated ground truth set. The results support previous findings from NLP research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps find duplicate economic research paper titles more efficiently using special computer language processing techniques. It tries different ways to match similar title phrases and uses tools like Levenshtein distance and cosine similarity to see how similar they are. Researchers found that many of these title duplicates don’t look very similar, which is good news! They checked their findings with a group of people who reviewed the titles again. |
Keywords
» Artificial intelligence » Cosine similarity » Natural language processing » Nlp
