4 citations · 4 across the 1 of their papers we have counts for
4 papers · 1 filter
SumHiS: Extractive Summarization Exploiting Hidden Structure
Tikhonov Pavel, Anastasiya Ianina, Valentin Malykh
Extractive summarization is a task of highlighting the most important parts of the text. We introduce a new approach to extractive summarization task using hidden clustering struct…
Answer Candidate Type Selection: Text-to-Text Language Model for Closed Book Question Answering Meets Knowledge Graphs
Mikhail Salnikov, Maria Lysyuk, Pavel Braslavski +3
Pre-trained Text-to-Text Language Models (LMs), such as T5 or BART yield promising results in the Knowledge Graph Question Answering (KGQA) task. However, the capacity of the model…
Large Language Models Meet Knowledge Graphs to Answer Factoid Questions
Mikhail Salnikov, Hai Le, Prateek Rajput +4
Recently, it has been shown that the incorporation of structured knowledge into Large Language Models significantly improves the results for a variety of NLP tasks. In this paper,…
How not to Lie with a Benchmark: Rearranging NLP Leaderboards
Shavrina Tatiana, Malykh Valentin
Comparison with a human is an essential requirement for a benchmark for it to be a reliable measurement of model capabilities. Nevertheless, the methods for model comparison could…