Papers by Erion Çano

2 papers
Keyphrase Generation: A Text Summarization Struggle (N19-1)

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Challenge: Existing methods for keyphrase generation are unable to produce valuable terms that do not appear in the text.
Approach: They propose to consider the keyphrase string as an abstractive summary of the title and the abstract.
Outcome: The proposed method can generate better keyphrases than the existing methods or the unsupervised ones.
Two Huge Title and Keyword Generation Corpora of Research Articles (2020.lrec-1)

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Challenge: Recent advances in sequence-to-sequence learning with neural networks have improved the quality of automatically generated text summaries and document keywords.
Approach: They propose to use OAGSX and OAGKX datasets to analyze text summaries and document keywords.
Outcome: The proposed models perform better than previous models on two large datasets . the authors hope to use the results to derive subsets of research articles from more disciplines .

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