Forum discussion data differ in both structure and properties from generic
form of textual data such as news. Henceforth, summarization techniques should,
in turn, make use of such differences, and craft models that can benefit from
the structural nature of discussion data. In this work, we propose attending to
the beginning of a document, to improve the performance of extractive
summarization models when applied to forum discussion data. Evaluations
demonstrated that with the help of bidirectional attention mechanism, attending
to the beginning of a document (initial comment/post) in a discussion thread,
can introduce a consistent boost in ROUGE scores, as well as introducing a new
State Of The Art (SOTA) ROUGE scores on the forum discussions dataset.
Additionally, we explored whether this hypothesis is extendable to other
generic forms of textual data. We make use of the tendency of introducing
important information early in the text, by attending to the first few
sentences in generic textual data. Evaluations demonstrated that attending to
introductory sentences using bidirectional attention, improves the performance
of extractive summarization models when even applied to more generic form of
textual data.

本文提出使用双向注意机制，关注讨论帖子中的开头（初始评论 / 帖子），可以引入一致的 ROUGE 分数提升，并在论坛讨论数据集上引入新的 ROUGE 分数的最新技术，同时发现此假设是否可扩展到其他通用形式的文本数据，通过考虑文本中前几句话，关注开头句，使用双向注意机制可以让摘要提取模型在更通用的文本数据上性能得到提升。