  The abundance of situational information on Twitter poses a challenge for
users to manually discern vital and relevant information during disasters. A
concise and human-interpretable overview of this information helps
decision-makers in implementing efficient and quick disaster response. Existing
abstractive summarization approaches can be categorized as sentence-based or
key-phrase-based approaches. This paper focuses on sentence-based approach,
which is typically implemented as a dual-phase procedure in literature. The
initial phase, known as the extractive phase, involves identifying the most
relevant tweets. The subsequent phase, referred to as the abstractive phase,
entails generating a more human-interpretable summary. In this study, we adopt
the methodology from prior research for the extractive phase. For the
abstractive phase of summarization, most existing approaches employ deep
learning-based frameworks, which can either be pre-trained or require training
from scratch. However, to achieve the appropriate level of performance, it is
imperative to have substantial training data for both methods, which is not
readily available. This work presents an Abstractive Tweet Summarizer (ATSumm)
that effectively addresses the issue of data sparsity by using auxiliary
information. We introduced the Auxiliary Pointer Generator Network (AuxPGN)
model, which utilizes a unique attention mechanism called Key-phrase attention.
This attention mechanism incorporates auxiliary information in the form of
key-phrases and their corresponding importance scores from the input tweets. We
evaluate the proposed approach by comparing it with 10 state-of-the-art
approaches across 13 disaster datasets. The evaluation results indicate that
ATSumm achieves superior performance compared to state-of-the-art approaches,
with improvement of 4-80% in ROUGE-N F1-score.
