Papers by Georgiana Ifrim
Examining the State-of-the-Art in News Timeline Summarization (2020.acl-main)
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| Challenge: | Existing work on news timeline summarization (TLS) has left an unclear picture of how well it is currently solved and how it can be approached. |
| Approach: | They propose a combination of different TLS strategies that improves over the stateof-the-art on all tested benchmarks. |
| Outcome: | The proposed method improves over the state-of-the-art on all tested benchmarks. |
Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement Learning (2022.acl-long)
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| Challenge: | Recent unsupervised sentence compression approaches use custom objectives to guide discrete search, but guided search is expensive at inference time. |
| Approach: | They propose to use reinforcement learning to train effective sentence compression models that are also fast when generating predictions. |
| Outcome: | The proposed model outperforms other unsupervised models while being faster at inference time. |
A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal (2020.acl-main)
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| Challenge: | Multidocument summarization (MDS) aims to compress large document collections into short summaries. |
| Approach: | They propose a large-scale multidocument summarization dataset that is large both in total number of document clusters and in the size of individual clusters. |
| Outcome: | The proposed dataset is large both in the total number of document clusters and in the size of individual clusters. |