Papers by Hideaki Tamori

2 papers
Transformer-based Lexically Constrained Headline Generation (2021.emnlp-main)

Copied to clipboard

Challenge: Existing automatic headline generation methods cannot include a given phrase in the generated headline.
Approach: They propose a Transformer-based method that guarantees to include a given phrase in a generated headline.
Outcome: The proposed method achieves ROUGE scores comparable to previous methods with Japanese news corpus.
A Japanese News Simplification Corpus with Faithfulness (2024.lrec-main)

Copied to clipboard

Challenge: Existing simplified corpora lack faithfulness to original text, resulting in errors in translation.
Approach: They propose to simplify Japanese newspaper articles to prioritize faithfulness over automated models.
Outcome: The proposed corpus preserves the original text, surpassing existing corpora.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations