Jingun Kwon


2023

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Abstractive Document Summarization with Summary-length Prediction
Jingun Kwon | Hidetaka Kamigaito | Manabu Okumura
Findings of the Association for Computational Linguistics: EACL 2023

Recently, we can obtain a practical abstractive document summarization model by fine-tuning a pre-trained language model (PLM). Since the pre-training for PLMs does not consider summarization-specific information such as the target summary length, there is a gap between the pre-training and fine-tuning for PLMs in summarization tasks. To fill the gap, we propose a method for enabling the model to understand the summarization-specific information by predicting the summary length in the encoder and generating a summary of the predicted length in the decoder in fine-tuning. Experimental results on the WikiHow, NYT, and CNN/DM datasets showed that our methods improve ROUGE scores from BART by generating summaries of appropriate lengths. Further, we observed about 3.0, 1,5, and 3.1 point improvements for ROUGE-1, -2, and -L, respectively, from GSum on the WikiHow dataset. Human evaluation results also showed that our methods improve the informativeness and conciseness of summaries.

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Hierarchical Label Generation for Text Classification
Jingun Kwon | Hidetaka Kamigaito | Young-In Song | Manabu Okumura
Findings of the Association for Computational Linguistics: EACL 2023

2021

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Making Your Tweets More Fancy: Emoji Insertion to Texts
Jingun Kwon | Naoki Kobayashi | Hidetaka Kamigaito | Hiroya Takamura | Manabu Okumura
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

In the social media, users frequently use small images called emojis in their posts. Although using emojis in texts plays a key role in recent communication systems, less attention has been paid on their positions in the given texts, despite that users carefully choose and put an emoji that matches their post. Exploring positions of emojis in texts will enhance understanding of the relationship between emojis and texts. We extend an emoji label prediction task taking into account the information of emoji positions, by jointly learning the emoji position in a tweet to predict the emoji label. The results demonstrate that the position of emojis in texts is a good clue to boost the performance of emoji label prediction. Human evaluation validates that there exists a suitable emoji position in a tweet, and our proposed task is able to make tweets more fancy and natural. In addition, considering emoji position can further improve the performance for the irony detection task compared to the emoji label prediction. We also report the experimental results for the modified dataset, due to the problem of the original dataset for the first shared task to predict an emoji label in SemEval2018.

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A New Surprise Measure for Extracting Interesting Relationships between Persons
Hidetaka Kamigaito | Jingun Kwon | Young-In Song | Manabu Okumura
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

One way to enhance user engagement in search engines is to suggest interesting facts to the user. Although relationships between persons are important as a target for text mining, there are few effective approaches for extracting the interesting relationships between persons. We therefore propose a method for extracting interesting relationships between persons from natural language texts by focusing on their surprisingness. Our method first extracts all personal relationships from dependency trees for the texts and then calculates surprise scores for distributed representations of the extracted relationships in an unsupervised manner. The unique point of our method is that it does not require any labeled dataset with annotation for the surprising personal relationships. The results of the human evaluation show that the proposed method could extract more interesting relationships between persons from Japanese Wikipedia articles than a popularity-based baseline method. We demonstrate our proposed method as a chrome plugin on google search.

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Considering Nested Tree Structure in Sentence Extractive Summarization with Pre-trained Transformer
Jingun Kwon | Naoki Kobayashi | Hidetaka Kamigaito | Manabu Okumura
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Sentence extractive summarization shortens a document by selecting sentences for a summary while preserving its important contents. However, constructing a coherent and informative summary is difficult using a pre-trained BERT-based encoder since it is not explicitly trained for representing the information of sentences in a document. We propose a nested tree-based extractive summarization model on RoBERTa (NeRoBERTa), where nested tree structures consist of syntactic and discourse trees in a given document. Experimental results on the CNN/DailyMail dataset showed that NeRoBERTa outperforms baseline models in ROUGE. Human evaluation results also showed that NeRoBERTa achieves significantly better scores than the baselines in terms of coherence and yields comparable scores to the state-of-the-art models.

2020

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Hierarchical Trivia Fact Extraction from Wikipedia Articles
Jingun Kwon | Hidetaka Kamigaito | Young-In Song | Manabu Okumura
Proceedings of the 28th International Conference on Computational Linguistics

Recently, automatic trivia fact extraction has attracted much research interest. Modern search engines have begun to provide trivia facts as the information for entities because they can motivate more user engagement. In this paper, we propose a new unsupervised algorithm that automatically mines trivia facts for a given entity. Unlike previous studies, the proposed algorithm targets at a single Wikipedia article and leverages its hierarchical structure via top-down processing. Thus, the proposed algorithm offers two distinctive advantages: it does not incur high computation time, and it provides a domain-independent approach for extracting trivia facts. Experimental results demonstrate that the proposed algorithm is over 100 times faster than the existing method which considers Wikipedia categories. Human evaluation demonstrates that the proposed algorithm can mine better trivia facts regardless of the target entity domain and outperforms the existing methods.