Papers by Kristjan Arumae
Towards Annotating and Creating Summary Highlights at Sub-sentence Level (D19-54)
Copied to clipboard
| Challenge: | Creating summary highlights at the sub-sentence level is desirable because sub-entrances are more concise than whole sentences. |
| Approach: | They propose to annotate summary-worthy sub-sentences and teach classifiers to do the same . they frame task as selecting important sentences and identifying a single most informative textual unit from each sentence. |
| Outcome: | The proposed method reduces the complexity involved in sentence compression by reducing the number of words and phrases deleted. |
An Empirical Investigation Towards Efficient Multi-Domain Language Model Pre-training (2020.emnlp-main)
Copied to clipboard
| Challenge: | Pre-training large language models is a standard practice in the natural language processing community. |
| Approach: | They propose to use elastic weight consolidation to mitigate catastrophic forgetting when pre-trained large language models are evaluated on generic benchmarks. |
| Outcome: | The proposed model achieves state-of-the-art on out-of domain tasks with minimal pre-training . elastic weight consolidation provides best overall scores yielding only a 0.33% drop in performance across seven generic tasks while remaining competitive in bio-medical tasks. |
Guiding Extractive Summarization with Question-Answering Rewards (N19-1)
Copied to clipboard
| Challenge: | a primary challenge faced by extractive summarization systems is the lack of annotated data. |
| Approach: | They propose a supervised extractive summarization system that rewards question-answering by identifying salient sequences of words from a document and highlighting them in the text. |
| Outcome: | The proposed system compares with baselines of strong summarization and human assessors on question-answering. |
Reinforced Extractive Summarization with Question-Focused Rewards (P18-3)
Copied to clipboard
| Challenge: | Existing methods for extractive summarization use human abstracts to create annotations for extraction units. |
| Approach: | They propose a method where abstracts are converted to Cloze-style comprehension questions to generate extractive summarization. |
| Outcome: | The proposed method surpasses state-of-the-art systems on the standard summarization dataset. |