Combining Deep Neural Reranking and Unsupervised Extraction for
Multi-Query Focused Summarization
- URL: http://arxiv.org/abs/2302.01148v1
- Date: Thu, 2 Feb 2023 15:08:25 GMT
- Title: Combining Deep Neural Reranking and Unsupervised Extraction for
Multi-Query Focused Summarization
- Authors: Philipp Seeberger, Korbinian Riedhammer
- Abstract summary: CrisisFACTS Track aims to tackle challenges such as multi-stream fact-finding in the domain of event tracking.
We propose a combination of retrieval, reranking, and incorporating the well-known Linear Programming (ILP) and Maximal Marginal Relevance (MMR) frameworks.
- Score: 0.30458514384586394
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The CrisisFACTS Track aims to tackle challenges such as multi-stream
fact-finding in the domain of event tracking; participants' systems extract
important facts from several disaster-related events while incorporating the
temporal order. We propose a combination of retrieval, reranking, and the
well-known Integer Linear Programming (ILP) and Maximal Marginal Relevance
(MMR) frameworks. In the former two modules, we explore various methods
including an entity-based baseline, pre-trained and fine-tuned Question
Answering systems, and ColBERT. We then use the latter module as an extractive
summarization component by taking diversity and novelty criteria into account.
The automatic scoring runs show strong results across the evaluation setups but
also reveal shortcomings and challenges.
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