  Even for us, it can be challenging to comprehend the meaning of songs. As
part of this project, we explore the process of generating the meaning of
songs. Despite the widespread use of text-to-text models, few attempts have
been made to achieve a similar objective. Songs are primarily studied in the
context of sentiment analysis. This involves identifying opinions and emotions
in texts, evaluating them as positive or negative, and utilizing these
evaluations to make music recommendations. In this paper, we present a
generative model that offers implicit meanings for several lines of a song. Our
model uses a decoder Transformer architecture GPT-2, where the input is the
lyrics of a song. Furthermore, we compared the performance of this architecture
with that of the encoder-decoder Transformer architecture of the T5 model. We
also examined the effect of different prompt types with the option of appending
additional information, such as the name of the artist and the title of the
song. Moreover, we tested different decoding methods with different training
parameters and evaluated our results using ROUGE. In order to build our
dataset, we utilized the 'Genious' API, which allowed us to acquire the lyrics
of songs and their explanations, as well as their rich metadata.
