Papers by Kazunari Sugiyama
Predicting Helpful Posts in Open-Ended Discussion Forums: A Neural Architecture (N19-1)
Copied to clipboard
| Challenge: | Unlike Community Question Answering, where questions are mostly factoid based, forum threads are often open-ended and contain repetitive or irrelevant posts. |
| Approach: | They propose a recurrent neural network-based architecture to model the relevance of a post regarding the original post starting the thread and the novelty it brings to the discussion. |
| Outcome: | The proposed model outperforms the state-of-the-art models for text classification on different types of online forum datasets. |
Identifying Emergent Research Trends by Key Authors and Phrases (C18-1)
Copied to clipboard
| Challenge: | Existing methods to identify emergent research trends have been employed to generate corpora of large corporata. |
| Approach: | They propose an embedded trend detection framework which integrates hypothesis that important phrases are written by important authors within a field and vice versa. |
| Outcome: | The proposed framework outperforms baselines based on text centrality or citations over two large datasets of scientific articles. |
Multi-TimeLine Summarization (MTLS): Improving Timeline Summarization by Generating Multiple Summaries (2021.acl-long)
Copied to clipboard
| Challenge: | Existing work on Time-Line Summarization (TLS) has focused on improving the performance of summarization but its drawbacks are as follows: a homogeneous dataset makes it hard to generalize; output is usually a single timeline regardless of the size and complexity of the input dataset. |
| Approach: | They propose a task that generates a time-line for each story given a news article . they propose MTLS task that can be generalized to other news articles . |
| Outcome: | The proposed task can generate bet-ter results than Time-Line Summarization (TLS) the proposed task is based on previous evaluation methods. |