  High-level understanding of stories in video such as movies and TV shows from
raw data is extremely challenging. Modern video question answering (VideoQA)
systems often use additional human-made sources like plot synopses, scripts,
video descriptions or knowledge bases. In this work, we present a new approach
to understand the whole story without such external sources. The secret lies in
the dialog: unlike any prior work, we treat dialog as a noisy source to be
converted into text description via dialog summarization, much like recent
methods treat video. The input of each modality is encoded by transformers
independently, and a simple fusion method combines all modalities, using soft
temporal attention for localization over long inputs. Our model outperforms the
state of the art on the KnowIT VQA dataset by a large margin, without using
question-specific human annotation or human-made plot summaries. It even
outperforms human evaluators who have never watched any whole episode before.
