The Seq2Seq model for abstractive text summarization consists of two main components: an encoder and a decoder. The encoder takes in the input text and generates a fixed-length vector representation, or "encoding", that captures the meaning of the input text. The decoder then takes in the encoding and generates the summary text one word or phrase at a time, using a "beam search" algorithm to select the most likely next word at each step. One of the key challenges in abstractive summarization is ensuring that the generated summary is both informative and coherent. To address this challenge, Seq2Seq models often incorporate attention mechanisms that allow the decoder to focus on different parts of the input text at each step, based on their relevance to the current word being generated. This helps to ensure that the summary is grounded in the input text and captures the most important information.
To improve the quality of the generated summaries, various techniques can be used, such as attention mechanisms, beam search, and reinforcement learning. Attention mechanisms allow the decoder network to focus on different parts of the input text when generating the summary. Beam search is a search algorithm that can be used to find the best summary from a set of possible summaries generated by the decoder network. Reinforcement learning can be used to fine-tune the Seq2Seq model by rewarding it for generating summaries that are similar to human-generated summaries.