Query-focused Extractive Summarisation for Biomedical and COVID-19 Complex Question Answering

• URL: http://arxiv.org/abs/2209.01815v1
• Date: Mon, 5 Sep 2022 07:56:44 GMT
• Title: Query-focused Extractive Summarisation for Biomedical and COVID-19 Complex Question Answering
• Authors: Diego Moll\'a (Macquarie University, Sydney, Australia)
• Abstract summary: This paper presents Macquarie University's participation in the two most recent BioASQ Synergy Tasks. We apply query-focused extractive summarisation techniques to generate complex answers to biomedical questions. For the Synergy task, we selected the candidate sentences following two phases: document retrieval and snippet retrieval. We observed an improvement of results when the system was trained on the second half of the BioASQ10b training data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents Macquarie University's participation to the two most recent BioASQ Synergy Tasks (as per June 2022), and to the BioASQ10 Task~B (BioASQ10b), Phase~B. In these tasks, participating systems are expected to generate complex answers to biomedical questions, where the answers may contain more than one sentence. We apply query-focused extractive summarisation techniques. In particular, we follow a sentence classification-based approach that scores each candidate sentence associated to a question, and the $n$ highest-scoring sentences are returned as the answer. The Synergy Task corresponds to an end-to-end system that requires document selection, snippet selection, and finding the final answer, but it has very limited training data. For the Synergy task, we selected the candidate sentences following two phases: document retrieval and snippet retrieval, and the final answer was found by using a DistilBERT/ALBERT classifier that had been trained on the training data of BioASQ9b. Document retrieval was achieved as a standard search over the CORD-19 data using the search API provided by the BioASQ organisers, and snippet retrieval was achieved by re-ranking the sentences of the top retrieved documents, using the cosine similarity of the question and candidate sentence. We observed that vectors represented via sBERT have an edge over tf.idf. BioASQ10b Phase B focuses on finding the specific answers to biomedical questions. For this task, we followed a data-centric approach. We hypothesised that the training data of the first BioASQ years might be biased and we experimented with different subsets of the training data. We observed an improvement of results when the system was trained on the second half of the BioASQ10b training data.

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