Papers by Richard Hahnloser
GreedyCAS: Unsupervised Scientific Abstract Segmentation with Normalized Mutual Information (2023.emnlp-main)
Copied to clipboard
| Challenge: | Abstracts of scientific papers typically contain premises and conclusions, but in non-structured abstracts the concluding information is not marked. |
| Approach: | They propose to use Normalized Mutual Information (NMI) to optimize the NMI score between two segments by assuming that conclusions are strongly semantically linked with preceding premises. |
| Outcome: | The proposed approach outperforms baseline methods on structured abstracts and on non-structured abstracts. |
Evaluating Unsupervised Argument Aligners via Generation of Conclusions of Structured Scientific Abstracts (2024.eacl-short)
Copied to clipboard
| Challenge: | Scientific abstracts provide a concise summary of research findings. |
| Approach: | They evaluate unsupervised approaches for extracting scientific arguments as aligned premise-conclusion pairs . they find mutual information outperforms other measures on this task . |
| Outcome: | The proposed methods outperform language models on the task of extracting scientific arguments from abstracts. |
Abstractive Document Summarization without Parallel Data (2020.lrec-1)
Copied to clipboard
| Challenge: | Abstractive summarization typically relies on large collections of paired articles and summaries. |
| Approach: | They propose a system that relies only on example summaries and non-matching articles . they use an unsupervised sentence extractor that selects salient sentences . |
| Outcome: | The proposed system performs well on CNN/DailyMail benchmark and automatic generating a press release from a scientific journal article. |
MemSum: Extractive Summarization of Long Documents Using Multi-Step Episodic Markov Decision Processes (2022.acl-long)
Copied to clipboard
| Challenge: | MemSum is a reinforcement-learning-based extractive summarizer that considers the text content of the sentence, the global context of the rest of the document, and the extraction history of the sentences that have already been extracted. |
| Approach: | They propose a reinforcement-learning-based extractive summarizer that iteratively selects sentences from a broad set of information that would intuitively be used by humans. |
| Outcome: | The proposed extractive summarizer is enriched with information on the extraction history and local, global, and historical information. |