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Speech summarization condenses long speech while preserving essential content. Recently, there has been growing interest in end-to-end (E2E) abstractive speech summarization, which directly generates a text summary from spoken input. However, abstractive summarization of lengthy speech sequences presents challenges, such as identifying key information within very long speech. In this paper, we hypothesize that first addressing the simpler task of extractive summarization can help with these aforementioned long-sequence challenges and improve overall summarization performance. To this end, we introduce an extractive-abstractive summarization model that exploits auxiliary information from extractive summaries generated directly from raw speech input to enhance abstractive speech summarization. Experiments on a web presentation corpus demonstrate consistent gains with our proposed method, achieving up to 1.4-point gains in METEOR score over a strong abstractive summarization baseline.